Mathieu Gonzalez
email: mathieu.gonzalez@b-com.com

Eric Marchand
email: eric.marchand@irisa.fr

Amine Kacete
email: amine.kacete@b-com.com

Jerome Royan
email: jerome.royan@b-com.com

TwistSLAM: Constrained SLAM in Dynamic Environment

Keywords: SLAM, Localization, Mapping

Classical visual simultaneous localization and mapping (SLAM) algorithms usually assume the environment to be rigid. This assumption limits the applicability of those algorithms as they are unable to accurately estimate the camera poses and world structure in real life scenes containing moving objects (e.g. cars, bikes, pedestrians, etc.). To tackle this issue, we propose TwistSLAM: a semantic, dynamic and stereo SLAM system that can track dynamic objects in the environment. Our algorithm creates clusters of points according to their semantic class. Thanks to the definition of inter-cluster constraints modeled by mechanical joints (function of the semantic class), a novel constrained bundle adjustment is then able to jointly estimate both poses and velocities of moving objects along with the classical world structure and camera trajectory. We evaluate our approach on several sequences from the public KITTI dataset and demonstrate quantitatively that it improves camera and object tracking compared to state-of-the-art approaches.

I. INTRODUCTION

V ISUAL Simultaneous Localization And Mapping (SLAM) is an important problem for robotics that has been heavily studied in the past decade. Its goal is to estimate the pose of a camera moving in a scene while building a map of the environment. Some algorithms such as [START_REF] Mur-Artal | ORB-SLAM2: An open-source SLAM system for monocular, stereo, and RGB-D cameras[END_REF], [START_REF] Engel | LSD-SLAM: Large-scale direct monocular SLAM[END_REF] can efficiently solve this problem, however they rely on the static scene assumption. This hypothesis assumes that the world is a single rigid body and thus that no object can move within it. This assumption, which is rarely met in most real world scenes, as they can contain moving objects (e.g. cars, bikes, pedestrians for urban scenes of the KITTI dataset), limits the scenarios in which a SLAM algorithm can be used. Classical SLAM systems such as [START_REF] Mur-Artal | ORB-SLAM2: An open-source SLAM system for monocular, stereo, and RGB-D cameras[END_REF] try to alleviate this assumption using robust estimators, allowing them to flag moving parts as outliers. However as soon as the number of moving points is too important, the estimated camera pose accuracy decreases. This makes this approach unsuitable for some scenes (e.g. crowded or urban scenes). Some systems [START_REF] Bescos | DynaSLAM: Tracking, mapping, and inpainting in dynamic scenes[END_REF], [START_REF] Yu | DS-SLAM: A semantic visual SLAM towards dynamic environments[END_REF] have been proposed to detect and mask out dynamic objects in images, thus making the static scene assumption valid. However some recent approaches Fig. 1. Our approach allows us to track objects in the scene such as cars. Here we can see: (a) the frame with semantic points and tracked clusters (orange cars) with their estimated speed. (b) a map top view with tracked clusters (orange cars), clusters trajectories (black spheres and lines), clusters twists (blue and purple lines), road points and plane (in green) and camera trajectory (green and blue frustums). (c) Map side view. The rotation part of the twists (blue lines) is perpendicular to the road plane, the translation part (purple lines) is parallel to the plane. [START_REF] Bescos | DynaSLAM II: Tightlycoupled multi-object tracking and SLAM[END_REF], [START_REF] Zhang | VDO-SLAM: a visual dynamic object-aware SLAM system[END_REF], [START_REF] Runz | Maskfusion: Real-time recognition, tracking and reconstruction of multiple moving objects[END_REF], [START_REF] Huang | ClusterVO: Clustering moving instances and estimating visual odometry for self and surroundings[END_REF] argue that moving objects represent valuable information that can be necessary for some applications. Most recent approaches trying to solve both SLAM and object tracking have used semantics as an additional source of information. Semantic knowledge can indeed be beneficial to SLAM [START_REF] Cadena | Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age[END_REF], [START_REF] Rosinol | Kimera: an open-source library for real-time metric-semantic localization and mapping[END_REF] as it contains information about the class dynamicity [START_REF] Bescos | DynaSLAM: Tracking, mapping, and inpainting in dynamic scenes[END_REF] which is higher level information than simple 3D points.

In this paper we present a stereo SLAM system called TwistSLAM, as we estimate objects' twists to track them and consider that objects are linked to each other through mechanical joints, similarly to joints linking different parts of a robot. An illustration of our algorithm is visible in figure 1: the camera pose is estimated simultaneously with all moving objects in the scene and the map structure (here the plane of the road) constrains the movement of objects. Our approach is based on ORB-SLAM2 [START_REF] Mur-Artal | ORB-SLAM2: An open-source SLAM system for monocular, stereo, and RGB-D cameras[END_REF] and S 3 LAM [START_REF] Gonzalez | S 3 LAM: Structured scene SLAM[END_REF]. In our work we use semantic information to build a map of clusters corresponding to objects in the scene. The clustering of the scene allows us to estimate the pose of the camera using static clusters only such as road or house. The other clusters that can be dynamic are tracked and their pose is updated in the map through the estimation of twists that represent their velocity. Most SLAM systems that can track dynamic objects directly estimate their pose through the minimization of a reprojection error function [START_REF] Bescos | DynaSLAM II: Tightlycoupled multi-object tracking and SLAM[END_REF], [START_REF] Huang | ClusterVO: Clustering moving instances and estimating visual odometry for self and surroundings[END_REF] or with 3D points registration [START_REF] Runz | Maskfusion: Real-time recognition, tracking and reconstruction of multiple moving objects[END_REF]. Doing so, the estimated pose of an object has 6 degrees of freedom. This does not correspond to reality. For example a car has only 3 degrees of freedom, 2 translations in the road plane and 1 rotation around its normal, hence its pose should be constrained. Our goal is thus to remove degrees of freedom corresponding to physically unfeasible movements. To do so we choose to represent those constraints as mechanical joints which makes our approach highly generic. A mechanical joint between clusters constrains the estimated twist of a dynamic cluster by blocking some of its degrees of freedom thus reducing the effect of noise on the estimation. Once an object twist has been estimated it can be used to update the object pose which enables object tracking. The object poses can then be tightly refined with camera poses and 3D points within a bundle adjustment that also applies mechanical joints constraints.

Our contributions presented in this paper are:

• A semantic SLAM system that can robustly estimate the pose of a camera in static as well as dynamic scenes. • A stereo SLAM framework that can track multiple moving objects in the scene. • A new formulation for both the tracking and bundle adjustment that takes into account the characteristics of mechanical joints between objects in the scene to constrain their movements. We also evaluate our approach on several sequences from the public KITTI dataset and compare our approach quantitatively with respect to ORB-SLAM2 [START_REF] Mur-Artal | ORB-SLAM2: An open-source SLAM system for monocular, stereo, and RGB-D cameras[END_REF], DynaSLAM [START_REF] Bescos | DynaSLAM: Tracking, mapping, and inpainting in dynamic scenes[END_REF], VDO-SLAM [START_REF] Zhang | VDO-SLAM: a visual dynamic object-aware SLAM system[END_REF] and DynaSLAM II [START_REF] Bescos | DynaSLAM II: Tightlycoupled multi-object tracking and SLAM[END_REF], which demonstrates the benefits of our method in terms of object poses and velocities and camera pose estimation accuracy. The rest of the paper is described as follows. First we describe related work on dynamic classical and semantic SLAM. Then we give an overview of mathematical concepts that will be used in this paper. Following, we describe our approach to build a semantic map of clusters, estimate the pose of a camera in a dynamic scene, track moving clusters within the scene while using joint constraints to improve object tracking and refine all estimations with a bundle adjustment. Finally we demonstrate the benefits of our approach on multiple sequences from a public dataset.

II. RELATED WORK: DYNAMIC SLAM

In this section we first present some classical [START_REF] Mur-Artal | ORB-SLAM2: An open-source SLAM system for monocular, stereo, and RGB-D cameras[END_REF] and dynamic SLAM systems [START_REF] Li | RGB-D SLAM in dynamic environments using static point weighting[END_REF] that remove dynamic outliers. Then we focus on semantic SLAM systems that tackle the problem of dynamic objects by masking out [START_REF] Bescos | DynaSLAM: Tracking, mapping, and inpainting in dynamic scenes[END_REF], [START_REF] Yu | DS-SLAM: A semantic visual SLAM towards dynamic environments[END_REF] or tracking [START_REF] Runz | Maskfusion: Real-time recognition, tracking and reconstruction of multiple moving objects[END_REF], [START_REF] Bescos | DynaSLAM II: Tightlycoupled multi-object tracking and SLAM[END_REF], [START_REF] Huang | ClusterVO: Clustering moving instances and estimating visual odometry for self and surroundings[END_REF], [START_REF] Zhang | VDO-SLAM: a visual dynamic object-aware SLAM system[END_REF], [START_REF] Yang | CubeSLAM: Monocular 3-D object SLAM[END_REF] objects in the scene. Our approach belongs to this last class of systems. For a more in-depth survey of SLAM we refer the reader to [START_REF] Cadena | Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age[END_REF], [START_REF] Taketomi | Visual SLAM algorithms: A survey from 2010 to 2016[END_REF].

ORB-SLAM2 [START_REF] Mur-Artal | ORB-SLAM2: An open-source SLAM system for monocular, stereo, and RGB-D cameras[END_REF], which follows the work of [START_REF] Mouragnon | Real time localization and 3D reconstruction[END_REF], [START_REF] Klein | Parallel tracking and mapping for small AR workspaces[END_REF] uses three parallel threads to estimate the pose of a camera through relocalization or tracking, build a map, refine both using a bundle adjustment [START_REF] Triggs | Bundle adjustment-a modern synthesis[END_REF] and close looping trajectories. ORB-SLAM2 manages to mitigate the influence of dynamic objects through the use of a RANSAC scheme and a robust cost function based estimation. To tackle this problem some methods propose to roughly estimate the pose of the camera and robustly find outliers in the scene or in the image. Outliers are then removed or downweighted and the camera pose is refined. For example [START_REF] Li | RGB-D SLAM in dynamic environments using static point weighting[END_REF] proposes a direct approach based on the alignment of depth edges using an ICP scheme. For each point they robustly estimate a staticity confidence score which downweights dynamic objects and an intensity assisted ICP robsulty refines the pose using those weights. However those approaches fail when the amount of dynamic parts in the image is too high and are not able to track dynamic objects. DS-SLAM [START_REF] Yu | DS-SLAM: A semantic visual SLAM towards dynamic environments[END_REF] applies a geometrical moving consistency check on segmented areas. This score allows them to know which areas correspond to moving objects, which are then discarded for robust camera pose estimation and mapping. DynaSLAM [START_REF] Bescos | DynaSLAM: Tracking, mapping, and inpainting in dynamic scenes[END_REF] uses semantic information to segment a priori moving objects, which are not used for tracking and mapping. The segmentation is refined using depth information. This approach improves camera localization in dynamic scenes but deteriorates it when a priori moving objects are in reality static such as parked cars. Those approaches, similarly to the previous ones, are not able to track dynamic objects. MaskFusion [START_REF] Runz | Maskfusion: Real-time recognition, tracking and reconstruction of multiple moving objects[END_REF] is one of the first semantic dynamic SLAM that can track objects. Inspired from [START_REF] Rünz | Co-fusion: Real-time segmentation, tracking and fusion of multiple objects[END_REF] it makes use of 2D instance segmentation to detect objects in the scene and tracks them using both photometric and geometric information from an RGB-D camera. DynaSLAM II [START_REF] Bescos | DynaSLAM II: Tightlycoupled multi-object tracking and SLAM[END_REF] uses semantic information to detect objects. Object 3D points are represented in the object reference frame and used to estimate the object pose at all time by minimizing their reprojection error. ClusterVO [START_REF] Huang | ClusterVO: Clustering moving instances and estimating visual odometry for self and surroundings[END_REF] is similar to [START_REF] Bescos | DynaSLAM II: Tightlycoupled multi-object tracking and SLAM[END_REF], but they consider object detection (i.e. 2D bounding boxes) as input which is much faster to infer than dense masks. They also apply a cleaning procedure to improve dynamic keypoints matching and make sure that 2D points do not come from the background of the bounding box. VDO-SLAM [START_REF] Zhang | VDO-SLAM: a visual dynamic object-aware SLAM system[END_REF] proposes to use optical flow to track features extracted more densely than other systems, which allows them to obtain a more precise object pose estimation. Furthermore the optical flow and the object and camera motions are tightly refined. CubeSLAM [START_REF] Yang | CubeSLAM: Monocular 3-D object SLAM[END_REF] is different from the previously cited papers as it is an object based SLAM. A 3D bounding box is fitted for each object detected by a CNN (such as [START_REF] Redmon | You only look once: Unified, real-time object detection[END_REF]), which allows them to know the object 6 DoF pose and dimensions for each image. The bounding boxes poses are then optimized along with the camera poses in a single BA, similarly to [START_REF] Bescos | DynaSLAM II: Tightlycoupled multi-object tracking and SLAM[END_REF].

III. BACKGROUND ON TWISTS AND HOMOGENEOUS

TRANSFORMATIONS

In this section we present the mathematical concepts and notations that we use in this paper. A rigid object o, which can move in the 3D scene, can be associated with its coordinate frame F o . Its pose relative to the world coordinate frame F w can be represented by the homogeneous matrix in the i th frame

w T oi = w R oi w t oi 0 1 ∈ SE(3) (1)
where SE(3) denotes the special euclidean group. This matrix maps points expressed in the object coordinate frame and denoted o X to points expressed in the world coordinate frame and denoted w X, according to the following equation: w X = w T o o X. The velocity of a moving object can be represented using a twist ξ defined as

ξ = v x v y v z ω x ω y ω z ⊤ = v ω ⊤ ∈ R 6 (2)
where the first

3 components v = (v x , v y , v z ) ⊤ ∈ R 3
denote the translational velocity and the other components ω = (ω x , ω y , ω z ) ⊤ ∈ R 3 represent the rotational velocity. There exists a matrix representation for twists that can be obtained using the operator [.] ∧ defined by:

[ξ] ∧ = [ω] × v 0 0 ∈ se(3) (3)
where [.] × is the skew-symmetric operator defined such as for a = (a x , a y , a z ) ⊤ ∈ R 3 :

[a] × =   0 -a z a y a z 0 -a x -a y a x 0   (4)
and se( 3) is the Lie algebra associated to SE(3). We denote w ξ oi the twist corresponding to the velocity of the object o at frame i expressed in the world coordinate frame. Similarly, oi ξ oi is the velocity of the object o at frame i expressed in its own coordinate frame. As velocities can be integrated over time to obtain new positions, there exists a mapping from se(3) to SE(3) called the exponential map and denoted:

exp : [ξδt] ∧ ∈ se(3) -→ T ∈ SE(3) (5)
where δt is the time interval duration [START_REF] Blanco | A tutorial on se (3) transformation parameterizations and on-manifold optimization[END_REF].

Using the exponential map we can recover the pose of the object o moving according to the twist w ξ oi from its initial pose at frame i, w T oi to its next pose at frame i + 1, w T oi+1 using the following formula:

w T oi+1 = exp( w ξ oi δt i ) w T oi = w T oi exp( oi ξ oi δt i ) (6)
Note that the choice of coordinate frame matters, it can be useful to define an operator to change the coordinate frame of a twist. Such operator is called the adjoint map w V oi ∈ R 6×6 and links twists in different coordinate frames according to:

w ξ oi = w V oi oi ξ oi (7)
The adjoint map can be computed using the relative pose w T oi between F oi and F w :

w V oi = w R oi [ w t oi ] × w R oi 0 w R oi (8)
For simplicity we will consider in the remainder of this paper that δt = 1 without loss of generality.

IV. TWISTSLAM: CONSTRAINED SLAM IN DYNAMIC ENVIRONMENT

In this section we present our approach. Our general idea is to represent the world as a graph of semantic clusters, which is similar to a scene graph and can be seen in figure 2. The vertices of the graph correspond to objects in the scene and the edges to physical links that exist between objects. Our goal is to estimate the pose of the camera and the pose of every moving object while using mechanical joints between objects to improve those estimations. For example both clusters car in our graph are linked to the road with a planar constraint that allows only 3 degrees of freedom: a rotation around the normal of the plane and 2 translations within the plane. Such simple representations allow us to be highly generic as, for a given semantic class, we only need to define its static parent and the type of mechanical joint. The pipeline of our approach is presented in figure 3. Using semantic information we create clusters of points corresponding to objects in the scene. Then, we use static semantic clusters (e.g. road, floor, house) to robustly track the camera, even in dynamic scenes. Next, we match keypoints corresponding to dynamic objects (e.g., car, bike,...) to either track them or triangulate new 3D points using stereo information. All poses estimations from the camera and the objects are then refined with static and dynamic 3D points in a bundle adjustment process.

The main novelty of our approach comes from the fact that we optimize the velocities of dynamic objects rather than their pose and constrain the velocities according to mechanical joints between objects. This approach is highly generic as we only need to define a handful of joints (that correspond to normalized joints in mechanics) and a list of semantic classes pairs for each joint (e.g. the wall-door joint corresponds to a revolute joint, the car-road joint corresponds to a planar joint). As we will latter show, it allows us to remove displacements along directions that are not physically possible (e.g. a car translating vertically). This allows us to obtain a more precise estimation of the dynamic object poses.

A. Creating Clusters from panoptic segmentation

Most recent semantic dynamic SLAM systems use either an object detection or an instance segmentation algorithm. Working in the continuity of S 3 LAM [START_REF] Gonzalez | S 3 LAM: Structured scene SLAM[END_REF] we chose to estimate the panoptic segmentation of images, obtained using [START_REF] Wu | Detectron2[END_REF]. This allows us to know the semantic class of each pixel in the image and to give a unique id to each object. Similarly to [START_REF] Gonzalez | S 3 LAM: Structured scene SLAM[END_REF] we fuse multiple 2D observations of a single 3D point to obtain its class and id. Doing so we obtain a semantic map, which allows us to create a set of

K clusters O = {O k , k ∈ [1, K]}.
A cluster is a set of 3D points corresponding to a single object in the scene. Points are grouped according to their class and instance id. The set of clusters can be expressed as the set of a priori static clusters S (such as road, building, ...) and the set of a priori dynamic clusters D (such as car, bike, human, bus, ...). As static clusters are fixed, we represent their 3D points { w X} in the world frame. In contrast, each dynamic cluster contains a set of 3D points { o X} expressed in the object coordinate frame, a set of poses { o T w } and a set of twists { w ξ o } representing the cluster trajectory and velocity through time. For simplicity in the remainder of this paper we will omit the object index k as its use is straightforward.

B. Clusters geometry

Our goal is to constrain the velocity of moving clusters according to mechanical joints. To do so we need to estimate the pose of those joints. We propose to do this using the estimated geometry of some clusters. We chose to consider only planar clusters, which allows our approach to be highly generic as planes are common in man-made environment. For clusters corresponding to a priori chosen classes (such as the road or the facade of a building) we estimate a 3D plane, represented by π = (a, b, c, d) ⊤ with ||π|| 2 = 1, using only its 3D points { w X}. The plane follows the following equation: π ⊤w X = 0 and can be estimated using an SVD. To make it robust to outliers we use a RANSAC scheme.

C. Dynamic SLAM

As we do not know which dynamic objects in the scene are really moving we chose to estimate the camera pose using only static objects. Using points from static clusters we minimize the following cost function

E( ci T w ) = j∈S ρ(|| i x j -p( ci T w , w X j )|| Σ -1 i,j ) (9)
where i x j is the 2D keypoint corresponding to the observation of w X j in the i th frame, p is the pinhole camera projection function, ρ is a robust cost function (in our case Huber) [START_REF] Malis | Experiments with robust estimation techniques in real-time robot vision[END_REF] and Σ i,j is the covariance matrix of the reprojection error. Doing so the estimated camera pose does not take into account potentially moving objects, hence it is robust in dynamic scenes. However the estimation can be deteriorated in scenes that contain many potentially moving objects that are in reality static, like for example parked cars. To solve this problem, we chose to estimate the pose of all moving objects and integrate them in the bundle adjustment, so that the velocity of static objects is close to 0 and their points act as static points.

D. Dynamic data association and keypoints

Dynamic data association is a challenging problem for two reasons: first the combination of the camera and the object movements can produce large displacements in the image space thus needing a large radius search for keypoints matching. Second a large movement can cause an important visual variation of the object in the image (e.g. due to luminosity changes on the object or to viewpoint changes) which makes the matching process more difficult. To overcome those challenges we propose to use the optical flow estimation produced by a CNN [START_REF] Teed | Raft: Recurrent all-pairs field transforms for optical flow[END_REF] to have a good estimate of the keypoints location and reduce the search radius, thus reducing both search time and the probability of false matches.

One problem of object tracking compared to classical SLAM is that dynamic objects usually occupy a small portion of the image, leading to too few object points to obtain a precise estimation [START_REF] Bescos | DynaSLAM II: Tightlycoupled multi-object tracking and SLAM[END_REF], [START_REF] Zhang | VDO-SLAM: a visual dynamic object-aware SLAM system[END_REF]. To solve this problem we force the keypoint extraction process to keep more keypoints from areas defined by objects bounding boxes. The keypoints are then used either to create new 3D points with stereo triangulation, which are added to existing clusters or used to create new clusters, or used to track the existing cluster.

E. Mechanical joints as inter-cluster constraints

Using matches found by the data association process we seek to estimate the pose of dynamic objects in the scene. Our assumption in this work is that many moving clusters can be represented as being linked to a static parent cluster with a specific mechanical joint. There exist 12 normalized joints (ISO 3952) that can be associated with the degrees of freedom they have. For example the planar joint has 3 degrees of freedom: 2 translations in the plane and 1 rotation around its normal, this joint can represent the displacement of a car relative to its static parent, the road. Another example is the revolute joint which has a single degree of freedom corresponding to the rotation around a single axis. In this case the static parent cluster is the wall, the moving cluster is the door and its only possible movements are rotations around the axis of the joint (corresponding to the hinge).

To easily model all types of joints, similarly to [START_REF] Comport | Kinematic sets for realtime robust articulated object tracking[END_REF], we propose to decompose the space of twist as the sum of two orthogonal spaces:

R 6 = F l + F ⊤ l ( 10
)
where F l (which stands for freedom) is the space of twists allowed by the mechanical joint l with coordinate frame F l . In the case of a planar joint with axis z, F l is defined as:

F l = Span( 1 1 0 0 0 1 ⊤ ) ( 11
)
where Span is the linear span [START_REF] Axler | Linear algebra done right[END_REF]. In general we note:

F l = Span(A l ) (12)
where A l is a basis of F l . To make the displacement of an object physically accurate, its twists have to lie within the F l space. To do so we project the twist from its original space to F l . This is straightforward as R 6 is Euclidean, the operation projector is a 6 × 6 matrix defined as:

Π l = A l (A ⊤ l A l ) -1 A ⊤ l ( 13
)
In the example of a planar joint, it is easy to compute that:

Π l =        
1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

        (14)
in that case, a general twist can be projected such that:

Π l ξ = v x v y 0 0 0 ω z ⊤ ( 15
)
As we can see the only remaining degrees of freedom of the twist are coherent with the joint. Using this new constraint, we can modify the reprojection equation:

i x j = p( ci T w exp (Π w l ξ oi ) w T oi-1 , o X j ) (16)
However this equation is only true if the twist is expressed in the joint coordinate frame, yet according to [START_REF] Zhang | VDO-SLAM: a visual dynamic object-aware SLAM system[END_REF], it is naturally expressed either in the world or in the object coordinate frame.

To change the coordinate frame of a twist we can use the adjoint map defined in [START_REF] Huang | ClusterVO: Clustering moving instances and estimating visual odometry for self and surroundings[END_REF]. Hence the reprojection equation of the j th point in frame i becomes:

i x j = p( ci T w exp ( w V l Π l l V w w ξ oi ) w T oi-1 , o X j ) (17)
In the remainder of this paper we will note Π = w V l Π l l V w for simplicity. This equation takes a 3D point in the object frame, transforms it in the world frame using the previous object pose and multiplies it by the exponential of the current twist to get its current position. The twist is expressed in the joint coordinate frame with the adjoint map, projected using Π l to keep only the relevant components and expressed again in the world frame with the inverse adjoint map. Doing this we obtain a 3D point in the world frame for frame i with a transformation that perfectly respects the mechanical joint. We then apply the camera pose to obtain the point in the camera coordinate frame, which allows us to project it in the image.

Using the reprojection function we can estimate the twist corresponding to the transformation of a set of object points between frame i -1 and i by minimizing the following error:

E( oi ξ w )= j ρ(|| i x j -p( ci T w exp(Π w ξ oi ) w T oi-1 , o X j )|| Σ -1 i,j ) (18)
where ρ is the Huber robust estimator [START_REF] Malis | Experiments with robust estimation techniques in real-time robot vision[END_REF] and Σ i,j is the covariance matrix of the reprojection error. In [START_REF] Mur-Artal | ORB-SLAM2: An open-source SLAM system for monocular, stereo, and RGB-D cameras[END_REF] the convariance matrix depends on the scale at which the keypoints are observed. In our case we choose to estimate it using the median absolute deviation (MAD) [START_REF] Malis | Experiments with robust estimation techniques in real-time robot vision[END_REF] that is a robust estimator of the standard deviation of the reprojection error. We perform the optimization using the Levenberg-Marquardt algorithm on matches found between the current and the previous frame. Then we refine this twist with an approach similar to [START_REF] Mur-Artal | ORB-SLAM2: An open-source SLAM system for monocular, stereo, and RGB-D cameras[END_REF] by projecting map points, transformed with the estimated twist, in the current frame to search for additional matches and obtain a more accurate estimation. The object pose in frame i is then updated as w T oi = exp(Π w ξ oi ) w T oi-1 . This tracking procedure is repeated for all objects.

F. Dynamic Bundle Adjustment

The goal of classical bundle adjustment is to refine the camera trajectory and 3D points position estimation. The dynamic bundle adjustment has multiple goals. First, the refinement of the dynamic objects trajectory and their 3D points position, jointly with the camera trajectory and 3D static points position. Second, it allows to link the object and the camera trajectory, indeed if the bundle adjustment did not take into account dynamic objects, only the camera pose would have an impact on the object pose, which would not improve it. By taking into account dynamic points whose position is estimated over time we can use them to refine the camera pose, similarly to static points but with less accuracy since object pose estimation is more noisy. Finally, it allows us to apply a soft constrain on twists within a temporal window. Doing so we obtain smoother trajectories and velocities that are more physically plausible.

Our bundle adjustment cost function can be written as follows:

E({ w ξ o , c T w , w X, o X}) = i,j e i,j stat + i,j e i,j dyna + i e i const ( 19
) where e i,j stat is the classical static reprojection error:

e i,j stat = ρ(|| i x j -p( ci T w , w X j )|| Σ -1 i,j
) e i,j dyna is a dynamic reprojection error:

e i,j dyna = ρ(|| i x j -p( c T w exp(Π w ξ oi ) w T oi , o X)|| Σ -1 i,j )
where Σ -1 i,j is estimated using the MAD as in equation [START_REF] Rünz | Co-fusion: Real-time segmentation, tracking and fusion of multiple objects[END_REF]. And e i const is a constant velocity model that penalizes twists variations by linking 3 consecutive poses:

e i const = ρ(||Π w ξoi+1 -Π w ξoi || W )
where W is a diagonal weight matrix used to balance the errors, w ξoi+1 is the twist linking the poses exp(Π w ξ oi ) w T oi and exp(Π w ξ oi+1 ) w T oi+1 and w ξoi is the twist linking the poses exp(Π w ξ oi-1 ) w T oi-1 and exp(Π w ξ oi ) w T oi . Those twists are computed using the logmap from SE(3) to se(3) defined in [START_REF] Blanco | A tutorial on se (3) transformation parameterizations and on-manifold optimization[END_REF] and can be written for w ξoi+1 as:

w ξoi+1 = log(exp((Π w ξ oi+1 ) w T oi+1 )(exp(Π w ξ oi ) w T oi ) -1 )
This equation moves each pose while respecting the mechanical joints constraints. Optimizing it can be cumbersome however the Schur trick can be applied as its Hessian is sparse [START_REF] Bescos | DynaSLAM II: Tightlycoupled multi-object tracking and SLAM[END_REF]. These equations are classically optimized on a set of local keyframes that share visual information, but in our case, inspired by [START_REF] Huang | ClusterVO: Clustering moving instances and estimating visual odometry for self and surroundings[END_REF] we chose to have 2 sets of keyframes: temporal and spatial. All frames are converted to temporal keyframes to improve the tracking of fast moving objects and be able to track an object as soon as it enters the field of view of the camera. Keyframes stay in the temporal set for a fixed duration (in our case 5 seconds) they are then culled more severely than in ORB-SLAM2. This allows us to keep a reasonable number of keyframes. We chose to optimize camera poses on the set of temporal and local keyframes, while object poses are only optimized on the set of temporal keyframes and fixed in all other keyframes. Doing so, we apply our constant motion model only on the temporal window, allowing clusters to accelerate or decelerate.

G. Computing the cost functions jacobians

To optimize the cost functions ( 18) and ( 19) with a Levenberg-Marquardt optimizer we need to compute their jacobian. First we compute the Jacobian of the cost function used for object tracking E( oi ξ w ). Using the chain rule and getting inspiration from [START_REF] Blanco | A tutorial on se (3) transformation parameterizations and on-manifold optimization[END_REF] it can be shown that:

J E = ∂E( oi ξ w ) ∂ oi ξ w = ∂p( c X) ∂ c X ( c X ⊤ ⊗ I 3 )(I 4 ⊗ c R w )(I 3 ⊗ w T o )∂ exp Π ( 20
)
where ⊗ is the Kronecker product, I N is an identity matrix of size N , c R w is the rotation matrix of c T w and:

∂ exp = ∂ exp(ξ) ∂ξ =     0 3×3 -[e 1 ] × 0 3×3 -[e 2 ] × 0 3×3 -[e 3 ] × I 3 0 3×3     (21)
where {e 1 , e 2 , e 3 } is the canonical base of R 3 . Then, we compute the jacobian of e dyna in equation [START_REF] Redmon | You only look once: Unified, real-time object detection[END_REF], which is very similar to the previous jacobian. The derivatives of the function with respect to camera poses and points are the same as for classical bundle adjustment [START_REF] Dellaert | Visual slam tutorial: Bundle adjustment[END_REF]. For the object poses we can compute:

J e dyna = ∂e dyna ∂ oi ξ w = ∂p( c X) ∂ c X ( o X ⊤ ⊗ I 3 )(I 4 ⊗ c R w )(I 3 ⊗ w T o )∂ exp Π (22)
Finally we compute the jacobian of the constant velocity constrain with respect to each of the 3 twists involved in the constrain:

J econst = ∂econst ∂ w ξo i-1 ∂econst ∂ w ξo i ∂econst ∂ w ξo i+1 (23)
we only show here the left part of the jacobian as the other parts are similar.

∂e const ∂ w ξ oi-1 = ∂ log(T) ∂T (I 4 ⊗ R)∂ exp Π (24)
with T = exp((Π w ξ oi ) w T oi )(exp(Π w ξ oi-1 ) w T oi-1 ) -1 , R is the rotation matrix of exp((Π w ξ oi ) w T oi )( w T oi-1 ) -1 and the derivative of the logmap is given by [START_REF] Blanco | A tutorial on se (3) transformation parameterizations and on-manifold optimization[END_REF].

V. EXPERIMENTS

In this section we present the experiments we conducted to test our approach. We evaluate both the accuracy of the camera pose estimation and of the object pose estimation.

A. Experiments details

Datasets. We evaluate our approach on the KITTI [START_REF] Geiger | Are we ready for autonomous driving? the kitti vision benchmark suite[END_REF] tracking dataset which consists in multiple sequences recorded from a camera mounted on a car. This dataset is particularly interesting for our approach as it contains the ground truth for both the camera pose and for some objects poses such as vehicles. It should be noted that the segmentation network can yield an "unknown" class which we consider to be static, as the dynamic classes in the KITTI dataset (cars, bikes and pedestrians) are correctly segmented by the network. Metrics. The metrics for the evaluation of SLAM systems are usually the absolute translation error (ATE) [START_REF] Sturm | A benchmark for the evaluation of RGB-D SLAM systems[END_REF] and the relative pose error (RPE) [START_REF] Zhang | A tutorial on quantitative trajectory evaluation for visual (-inertial) odometry[END_REF]. For each sequence we report the translation and rotation parts of the RPE, as it is done by both VDO-SLAM and DynaSLAM2. The object pose estimation accuracy can be evaluated using 2 different types of metrics: on the one hand the ATE and RPE that measure the quality of the objects trajectories and on the other hand the MOTP that evaluates the per-frame accuracy of objects 3D bounding boxes estimations and that we compute similarly to [START_REF] Bescos | DynaSLAM II: Tightlycoupled multi-object tracking and SLAM[END_REF] using KITTI evaluation tools. As we do not estimate object boxes we use the ground truth box at the first pose of each object and propagate it using our camera and object pose estimations. We evaluate the true positive rate (TP) and the MOTP using the projected 3D bounding box (2D), in bird view (BV) and in 3D.

B. Camera pose estimation

In this subsection we evaluate the accuracy of our camera pose estimation. Similarly to [START_REF] Bescos | DynaSLAM II: Tightlycoupled multi-object tracking and SLAM[END_REF] we only show here sequences in which the camera is moving. As we can see in table I our approach improves camera pose estimation on several sequences. To evaluate the stability of our approach we also computed the standard deviation over 10 runs for the sequence 3 and obtained a value of 1 ×10 -4 and 4 × 10 -4 for the translation and the rotation respectively. As objects are often either small, only visible for a short time or static, [START_REF] Mur-Artal | ORB-SLAM2: An open-source SLAM system for monocular, stereo, and RGB-D cameras[END_REF] performs well, but as we track clusters using many points, with a good precision, especially for clusters that do not move, we are able to reduce the drift. [START_REF] Zhang | VDO-SLAM: a visual dynamic object-aware SLAM system[END_REF] also gives good results but requires depth information while our approach gives similar or better results than RGB based approaches. The most important improvement of our approach is in terms of object tracking accuracy as we can see in table II. In this subsection we evaluate the accuracy of our object pose estimation. As we can see we improve object tracking accuracy, particularly for static objects such as the car 35 from sequence 11. The most important improvements usually come from the rotational part of the RPE, which is understandable as we only have 1 degree of freedom for the rotation of cars. We also observe that the most challenging cases happen when an object starts and stays far from the camera (e.g. seq. 05 and 10) because object tracking uses 3D points triangulated from stereo matches that are imprecise when points are far from the camera. We argue that the bruteforce keypoint matching of [START_REF] Bescos | DynaSLAM II: Tightlycoupled multi-object tracking and SLAM[END_REF] help them when few frame to frame matches can be found, which can happen when the object is far from the camera. Furthermore we have not implemented a way to relocalize an object that has been lost for multiple frames. Thus on some sequences (e.g. car 0 of seq. 11 and car 12 of seq. 20) in which the objects are alternatively far and close from the camera, we are only able to track them on a small portion of their trajectory. However, we can see that we are generally able to accurately track objects for most of their trajectory. During our experiments we saw that we were able to track pedestrians, despite the fact that they are not rigid. We believe that our approach works because pedestrians undergo small deformations around arms and legs. As for camera pose estimation, we computed the standard deviation of object pose estimation for the sequence 3 and obtained values of 3.2 × 10 -2 , 2.8 × 10 -2 and 4.7 × 10 -2 for the APE, the translational RPE and the rotational RPE. We also evaluated our results with an ANOVA which shows a significant difference (p-values ≤ 0.1) for most of our experiments.

We also show some qualitative results for the mapping, the camera and object pose estimation. The results are visible in figure 4. We are able to track multiple objects on all sequences. The estimated speed is very close from the ground truth with a maximum difference of about 3 km/h which occurs when the object is far from the camera or when it is created. Looking at the bounding boxes we can see that they coincide and thus that the poses are well estimated for near and far objects. As we can see in the middle figure we can accurately track non rigid objects such as the cyclist, as long as most of their surface is rigid. We also show on the right an example of both a tracked static car and a car that slows down and speeds up. As we can see the estimated speed is close to the 0 for the static car, making the dynamic keypoints act like static ones.

VI. CONCLUSION

In this paper we proposed a new stereo semantic dynamic SLAM system called TwistSLAM, able to estimate both the pose of the camera as well as to track all dynamic objects in the scene. Using mechanical joints between clusters we can constrain objects movements to physically possible movements, which allows us to improve both camera and objects pose estimation compared to the state of the art.

Fig. 2 .

 2 Fig. 2. Example of semantic graph: dynamic clusters are linked to static parent clusters with mechanical joints such as planar or revolute.

Fig. 3 .

 3 Fig.3. The pipeline of our approach: static keypoints are extracted from a stereo image (a) and used for camera tracking (d), dynamic keypoints are extracted from bounding boxes within the stereo images (b) and matched using optical flow (c) with the previous frame to track dynamic objects (e). The keyframe is then segmented (f) to create new semantic map points and clusters. Finally the object and camera poses are jointly refined with the dynamic and static map points in a BA (g).

TABLE I CAMERA

 I POSE ESTIMATION COMPARISON ON THE KITTI TRACKING DATASET.

	seq	ORB-SLAM2 [1]	DynaSLAM [3]	VDO-SLAM (RGB-D) [6]	DynaSLAM2 [5]	Ours
		RPE t (m/f) RPE R (°/f) RPE t (m/f) RPE R (°/f) RPE t (m/f)	RPE R (°/f)	RPE t (m/f)	RPE R (°/f) RPE t (m/f) RPE R (°/f)
	00	0.04	0.06	0.04	0.06	0.07	0.07	0.04	0.06	0.04	0.05
	01	0.05	0.04	0.05	0.04	0.04	0.12	0.05	0.04	0.04	0.03
	02	0.04	0.03	0.04	0.03	0.02	0.04	0.04	0.02	0.03	0.03
	03	0.07	0.04	0.07	0.04	0.03	0.08	0.06	0.04	0.06	0.02
	04	0.07	0.06	0.07	0.06	0.05	0.11	0.07	0.06	0.06	0.04
	05	0.06	0.03	0.06	0.03	0.02	0.09	0.06	0.03	0.06	0.02
	06	0.02	0.04	0.02	0.04	0.05	0.02	0.02	0.01	0.02	0.04
	07	0.05	0.07	0.05	0.07	-	-	0.05	0.07	0.04	0.04
	08	0.08	0.04	0.08	0.04	-	-	0.10	0.04	0.07	0.03
	09	0.06	0.05	0.06	0.05	-	-	0.06	0.06	0.05	0.04
	10	0.07	0.04	0.07	0.04	-	-	0.07	0.03	0.07	0.03
	11	0.04	0.03	0.04	0.03	-	-	0.04	0.03	0.03	0.02
	13	0.04	0.05	0.04	0.05	-	-	0.04	0.04	0.03	0.04
	14	0.03	0.08	0.03	0.08	-	-	0.03	0.08	0.03	0.06
	18	0.05	0.03	0.05	0.03	0.02	0.07	0.05	0.02	0.04	0.02
	19	0.05	0.03	0.05	0.03	-	-	0.05	0.02	0.03	0.03
	20	0.11	0.07	0.05	0.04	0.03	0.17	0.07	0.04	0.04	0.03
	mean	0.055	0.046	0.051	0.045	-	-	0.053	0.041	0.044	0.034
	std	0.020	0.016	0.016	0.015	-	-	0.019	0.020	0.015	0.011
	C. Object pose estimation.


Xiang Yan

Aureĺie Cayla

Eric Devaux

Belkacem Otazaghine

Fabien Salaun

Polypropylene/Poly(vinyl alcohol) Blends Compatibilized with Kaolinite Janus Hybrid Particles and Their Transformation into Fibers

This study focuses on the adoption of hybrid Janus kaolinite particles into immiscible PP-PVA blends for melt spinning, as an extension for the textile field. The anisotropic grafting of kaolinite by octadecylphosphonic acid allowed the localization of the particles at the PP-PVA interface. Then surfacemodified PP porous fibers have been obtained by selective extraction of the PVA phase. The SEM observations and selective extraction experiments indicated that the unmodified kaolinite particles were localized within the PVA phase having limited impact on the PVA accessibility, while Janus-modified particles were distributed at the biphasic interface decreasing significantly the PVA accessibility. The volume average diameter of PVA within annealed blends is dramatically decreased from 73.3 to 13.6 μm by the addition of 5 wt % of Janus kaolinite particles, proving the strong compatibility effect. Gel-like behavior was also observed in rheological investigations for blends filled with modified kaolinite. The melt flow index (MFI) was tested for adapting the conditions of melt spinning. The PVA accessibility can reach more than 90%, and the interface-located fillers can enhance the Young's modulus of the biphasic fibers with 57% increment. The crystallization structure of the porous PP fibers was also influenced by the Janus kaolinite particles, with the enhancement of crystal perfection.

INTRODUCTION

Blending polymers is a common method to endow the materials with synergetic properties. 1,2 Nevertheless, most of the polymers show inherent immiscibility due to low mixing entropy, 3 resulting in heterogeneous morphology and deterioration of mechanical properties, caused by the interface as stress concentration point. 4 A surfactant is usually added to enhance the compatibilization of two phases. Block copolymers (BCPs) are one of the most conventional surfactants used. It works as a coupling agent between the two polymers forming a lot of effective joints. 5 However, it is easy to be detached from the interface under fierce shearing conditions. In addition, beyond the critical concentration, BCPs will saturate the interface, aggregate, and finally generate micelles into the bulk phases. 6 Recently, there has been an extensive investigation about nanoparticles for compatibilization of polymer blends. A lot of candidates with different shapes and dimensions were tested, such as spherical (silica nanoparticles, 7 carbon black, 8 and gold nanoparticles 9 ), cage-like (POSS 10 ), patchy (clay 11 and graphene [START_REF] Tong | Promoting compatibilization effect of graphene oxide on immiscible PS/PVDF blend via waterassisted mixing extrusion[END_REF] ), and one-dimensional (carbon nanotube (CNT) [START_REF] Chen | Design of superior conductive polymer composite with precisely controlling carbon nanotubes at the interface of a co-continuous polymer blend via a balance of π-π interactions and dipole-dipole interactions[END_REF] ) particles. The rigid core which is formed by particles at the polymer blend interface provides the Pickering effect, with the morphology stability against shearing and annealing conditions. [START_REF] Wang | Reactive Nanoparticles Compatibilized Immiscible Polymer Blends: Synthesis of Reactive SiO2 with Long Poly(methyl methacrylate) Chains and the in Situ Formation of Janus SiO2 Nanoparticles Anchored Exclusively at the Interface[END_REF] Apart from the role of compatibilization, the intrinsic functionality can also be combined with the biphasic material, such as electrical, [START_REF] Pan | Enhancing the electrical conductivity of carbon black-filled immiscible polymer blends by tuning the morphology[END_REF] thermal conductivity, [START_REF] Huang | Massive enhancement in the thermal conductivity of polymer composites by trapping graphene at the interface of a polymer blend[END_REF] and antibacterial properties. [START_REF] Mural | Polyolefin based antibacterial membranes derived from PE/PEO blends compatibilized with amine terminated graphene oxide and maleated PE[END_REF] However, it is often difficult for these particles to localize them at the interface. The localization of nanoparticles is tunable via tailoring their wettability when the thermodynamical factor plays the dominant role. Based on Young's model, [START_REF] Fenouillot | Uneven distribution of nanoparticles in immiscible fluids: Morphology development in polymer blends[END_REF] it can be concluded that the particles can only be distributed at the interface when the absolute value of the difference between the two interfacial tensions for the particle with bulk polymers is lower than that between the two phases. The harsh requirement needs a precise surface grafting for the nanoparticles often based on trial-and-error methods, and the specific surface-tailored particles are only suitable in particular biphasic systems.

To overcome the obstacle, Janus nanoparticles (JNPs) appear as a solution combining the advantages of nanoparticles (Pickering effect) and BCPs (amphiphilicity), with two different surface compositions. JNPs can strongly adsorb and orient at the biphasic polymer interface. According to the molecular simulations, it has been proved that the Janus particles exhibit a higher surface activity, compared with other homogeneously modified surfactants. [START_REF] Yang | Janus nanoparticles inside polymeric materials: interfacial arrangement toward functional hybrid materials[END_REF] JNPs still work efficiently under high shear conditions. [START_REF] Walther | Engineering Nanostructured Polymer Blends with Controlled Nanoparticle Location using Janus Particles[END_REF] Nowadays, there are bursting numbers of reports concerning the utilization of Janus particles into polymer blends. 4,[START_REF] Walther | Engineering Nanostructured Polymer Blends with Controlled Nanoparticle Location using Janus Particles[END_REF][START_REF] Bryson | Using Janus Nanoparticles To Trap Polymer Blend Morphologies during Solvent-Evaporation-Induced Demixing[END_REF][START_REF] Bahrami | The Impact of Janus Nanoparticles on the Compatibilization of Immiscible Polymer Blends under Technologically Relevant Conditions[END_REF][START_REF] Wang | Compatibilization of Immiscible Polymer Blends Using in Situ Formed Janus Nanomicelles by Reactive Blending[END_REF][START_REF] Parpaite | Janus hybrid silica/polymer nanoparticles as effective compatibilizing agents for polystyrene/polyamide-6 melted blends[END_REF][START_REF] Xu | Study on the Compatibilizing Effect of Janus Particles on Liquid Isoprene Rubber/Epoxy Resin Composite Materials[END_REF] The difficulty lies in the breakage of the symmetry of the particles, for which the industrialization is hard to be realized. One of the methods is to break the symmetry of the particle skeleton. Parpaite et al. [START_REF] Parpaite | Janus hybrid silica/polymer nanoparticles as effective compatibilizing agents for polystyrene/polyamide-6 melted blends[END_REF] synthesized a snowman-like Janus particle, combined with PS and SiO 2 , for compatibilization of polystyrene (PS)-polyamide-6 (PA6) blend. Xu et al. [START_REF] Xu | Study on the Compatibilizing Effect of Janus Particles on Liquid Isoprene Rubber/Epoxy Resin Composite Materials[END_REF] also utilized similar snowman-like particles with asymmetrical grafted chains for compatibilization of liquid isoprene rubber-epoxy resin. Nie et al. [START_REF] Nie | Enthalpy-Enhanced Janus Nanosheets for Trapping Nonequilibrium Morphology of Immiscible Polymer Blends[END_REF] grafted synthetic silica nanosheet with PS and polyisoprene (PI) chains in order to compatibilize PS-PI blend. As a matter of fact, some natural fillers show an inherent chemical composition asymmetry which leaves the possibility to modify regioselectively their surface. Most attention has been paid to the high-aspect-ratio clay derivatives, such as halloysite nanotubes (HNTs) and kaolinite. A halloysite nanotube owns silanol groups at the outer surface and aluminol groups at the inner surface. Sahnoune et al. [START_REF] Sahnoune | Inner surface modification of halloysite nanotubes and its influence on morphology and thermal properties of polystyrene/ polyamide-11 blends[END_REF] used PS regioselective functionalized halloysite for compatibilizing a PS-PA11 blend. Kaolinite is one kind of phyllosilicate (layered aluminosilicate) with the molecular formula of Al 2 Si 2 O 5 (OH) 4 . It possesses dioctahedral 1:1 layered silicate (polar lamellae) stacked and held together via hydrogen bonds. One tetrahedral layer of silica (SiO 4 , TS layer) is linked through common apical oxygen atoms to one octahedral layer (AlO 2 (OH) 4 , OS layer). [START_REF] Sato | First-principle study of polytype structures of 1:1 dioctahedral phyllosilicates[END_REF] Weiss et al. [START_REF] Weiss | Hybrid Janus particles based on polymer-modified kaolinite[END_REF] modified kaolinite with polymer chains to obtain hybrid Janus particles. The regioselective modifications were based for one side on simple cation exchange and for the other side on covalent grafting via catechol groups. The obtained Janus kaolinite particles grafted with PS and PMMA chains were used for the compatibilization of PS-PMMA blend in a solvent casting procedure. However, the modification of both sides of kaolinite seems complicated and costly, which is not beneficial for industrial production. Is it feasible to modify one single side to obtain Janus particles with significant hybrid properties? So far, there are not any reports about the use of single side modified kaolinite for compatibilizing immiscible polymer blends.

For textile industries, the melt spinning provides an availability of fabricating fibers from biphasic polymers. [START_REF] Zhang | Fabrication and properties of poly(l-lactide) nanofibers via blend sea-island melt spinning[END_REF][START_REF] Huang | Poly(glycolic acid) Nanofibers via Sea-Island Melt-Spinning[END_REF] The related technology (e.g., twisting, knitting, weaving) can be applied with melt-spun fibers, playing an essential role in all corners of the industry. To best of our knowledge, there is no study on the application of Janus particles in melt spinning until now. Herein, we make efforts to extend the application of Janus particles into textile areas. The fillers at the interface can not only tune the biphasic morphology but also provide a protocol for fabricating surface-modified material. Moreover, if one of the phases can be drastically removed, surface-modified porous scaffold embedded with particles can be obtained. A lot of water-soluble polymers are good candidates for the sacrificial phase, such as poly(vinyl alcohol) (PVA), 32 polyvinylpyrrolidone (PVP), [START_REF] Fakirov | Converting of Bulk Polymers Into Nanosized Materials With Controlled Nanomorphology[END_REF] sulfopolyester (SP), [START_REF] Soltani | Influence of rheology and surface properties on morphology of nanofibers derived from islands-in-thesea meltblown nonwovens[END_REF] and poly(ethylene oxide) (PEO). [START_REF] Mural | Polyolefin based antibacterial membranes derived from PE/PEO blends compatibilized with amine terminated graphene oxide and maleated PE[END_REF]7 The sacrificial polymer can even be reused for the repetitive production. [START_REF] Fakirov | Converting of Bulk Polymers Into Nanosized Materials With Controlled Nanomorphology[END_REF] There were few reports [START_REF] Rezanova | Morphology and rheology of nanofilled PP/PVA blends[END_REF] concerning the morphological control of PP-PVA blends by adding nanofillers. Therefore, our research group has made efforts to control the localization of isotropic silica nanoparticles within PP-PVA [START_REF] Yan | Microstructure Evolution of Immiscible PP-PVA Blends Tuned by Polymer Ratio and Silica Nanoparticles[END_REF] and manufactured the materials into fibers by melt spinning. It has been found that 70:30 wt % is an ideal ratio for manufacturing the fibers, with a good spinnability, a high fraction of PVA removal, as well as satisfactory mechanical performance.

In this study, to improve compatibility with PP, kaolinite has been modified to make a single side (the octahedral layer) of the particles hydrophobic via regioselective grafting of octadecyl (C 18 H 37 -) groups by using octadecylphosphonic acid (ODPA), which forms a stable bulk (aluminoalkyl)phosphonate. [START_REF] Gao | Self-Assembled Monolayers of Alkylphosphonic Acids on Metal Oxides[END_REF] The obtained Janus kaolinite particles were then first used in PP-PVA biphasic systems. The ternary blends were compounded by melt extrusion, and the localization of the fillers was observed by SEM. The rheological performance and the morphology of the blends were investigated. Afterward, the materials were manufactured into multifilament yarns by melt spinning technology. The evolution of PVA accessibility from extrudates to multifilaments was observed, as a clear indication of the biphasic morphology and extraction efficiency. The mechanical properties and aggregation structures of the obtained fibers were also considered. Octadecylphosphonic acid (ODPA) was supplied by Specific Polymers (Montpellier, France). Ethanol and acetone were purchased from Fisher Chemical. Kaolinite (PARALUX) with a specific surface area (BET) of 12.2 ± 1.5 m 2 g -1 , an aspect ratio α of 10, and an average primary particle size (d el ) of 0.8 μm was kindly supplied by Imerys (Paris, France) and denoted as KL. Surface treatment was carried out as follows: 100 g of kaolinites, 2.5 g of ODPA, and 300 mL of an ethanol/water mixture (96/4) were introduced in a 500 mL flask fitted with a condenser. The mixture was then stirred and heated at solvent reflux for 15 h. The mixture was then centrifuged to remove the liquid phase and washed three times with acetone. Finally, the modified clay was dried under vacuum before characterization. Surface treated kaolinite is referred as KJ.

EXPERIMENTAL SECTION

All of the raw materials were used as received. 2.2. Sample Preparation. 2.2.1. PP-PVA-Kaolinite Ternary Nanocomposites. All of the polymer blends were melt-mixed using a corotating intermeshing twin-screw extruder (Thermo Haake, a screw diameter of 16 mm, L/D = 25). The mass ratio of PP and PVA was fixed at 70/30 (wt %/wt %), whereas the weight fraction of particles (KL and KJ) was varied at two different fractions: 1 and 5 wt % in total. PP and PVA were dried in the oven at 80 °C overnight to eliminate the residual moisture inside the polymers. The temperature profiles were 160/170/180/190/200 °C in the five heating zones, with the screw rotational speed of 100 rpm. The continuously produced extrudates in the form of rods were transported by a conveyor equipped with the cooling air stream. Afterward, the cooled polymer rods were pelletized, ready for the further melt spinning. For the ternary blends, the melt extrusion procedures were segregated into two steps. First, the kaolinite nanoparticles were precompounded with PP; second, the prepared blends were extruded again with PVA to obtain the PP-PVA-kaolinite ternary blends.

The samples containing unmodified kaolinite (KL) and modified kaolinite (KJ) are denoted as PP 70 -PVA 30 -KL x and PP 70 -PVA 30 -KJ x , respectively. x represents the wt % of filler in the polymer blends. The blend without fillers (denoted as PP 70 -PVA 30 ) was compounded by one-step extrusion.

2.2.2. PP-PVA-Kaolinite Ternary Multifilament Fibers. The corresponding multifilament fibers were prepared from the obtained extrudates, via the melt-spinning machine (Busschaert model Spinboy I, Deerlijk, Belgium). Before melt spinning, the related extrudates were dried at 80 °C overnight to get rid of the moisture. The dried extrudates were introduced through the hopper and plasticized by a single screw. The temperatures along the barrel were regulated from 180 to 215 °C. The rotating speed of the volumetric pump was adjusted at 15 rpm. Hence, the molten polymer blends could be forced through two dies of 40 circular channels (diameter Ø of 1.2 mm) at a constant rate. Continuously fabricated multifilament yarns (80 monofilaments) were cooled through the air, followed by the treatment of finishing oil. Afterward, the multifilament fibers were rolled up onto two heated rolls (70-80 °C). Two distinct rotational speeds 100 rpm of the first roll and 200 rpm of the second roll were applied to realize a draw for the fibers. The theoretical drawing ratio (DR) can be settled as 2. The final winding speed was adapted to the same as the second draw roll.

2.2.3. Selective Extraction Experiment. In order to obtain the microporous PP material, the selective extraction experiment was conducted to remove the sacrificial PVA phase. The fraction of PVA would be recorded as a reference for the PVA continuity within the polymers. Water is an excellent selective solvent for PVA and has no impact to PP matrix. First, the samples (extrudates and multifilament fibers) of roughly 4 g and filter papers (dried at 80 °C, overnight) were weighed separately. The tested samples were immersed into a water batch under constant magnetic stirring for 5 h to ensure the thorough extraction. The solution along with the samples was then immediately poured onto the filter paper supported by a glass funnel. The remaining samples were rinsed by water several times and were left overnight at 50 °C. The dried porous samples can be obtained, and meanwhile, the PVA accessibility can be worked out by eq 1. The values were calculated by at least 2 times, and statistical data were acquired.

W W W W PVA accessibility degree(%) ( ) 100 i r f f i P V A ω = - - × × + (1)
where W i represents the initial weight of the dried samples, W r+f indicates the total weight of samples jointed with the filter papers after the selective extraction experiment and drying, W f represents the weight of the dried filter papers prior to use, and ω PVA refers to the mass fraction of PVA within the polymer blends.

The synthesis of Janus kaolinite particles and the manufacturing process of surface-modified porous PP fibers are schematically represented in Figure 1.

2.3. Characterization. 2.3.1. Thermogravimetric Analysis (TGA). The thermal stability of the fillers was tested by thermal gravimetric analyzer (Mettler Toledo). In order to get rid of the physisorbed water, an isothermal step with a temperature of 110 °C lasted for 10 min prior to conducting the analysis. Afterward, the tested samples were heated to 900 °C at a rate of 10 °C/min. TGA was carried out under two different atmospheres, nitrogen and air.

Scanning Electron Microscopy (SEM).

The micromorphology observation of PP-PVA extrudates was conducted via a scanning electron microscope (FEI Quanta 200 ESEM). The sample was put in liquid nitrogen and broken by a hammer blow. The debris was placed on a pad of SEM and then metalized before analysis.

The morphology of the PP-PVA plate after rheometer measurement was also observed by an SEM apparatus (Inspect F, FEI Company). The dispersed PVA droplet sizes were determined by ImageJ analysis software (National Institutes of Health (NIH), Bethesda, MD, U.S.A.). More than 300 droplets were taken into account to ensure the accuracy. Afterward, Schwartz-Saltikov transformation [START_REF] Saltikov | The Determination of the Size Distribution of Particles in an Opaque Material from a Measurement of the Size Distribution of Their Sections[END_REF] method was applied to correct the size distributions of the droplets. The method was operated by dividing the distribution into 7 linear range groups, and the midpoint of each group represents the average value. The 3D particle distribution in volume can be transferred from the 2D particle distribution in area by applying the transformation coefficients.

2.3.3. Rheological Investigations. The rheological behaviors were surveyed on a rotational rheometer (AR 2000ex, TA Instruments, New Castle, DE, U.S.A.). The mode is selected as oscillatory characterization. The diameter of the parallel plate was 25 mm, and the gap distance was adjusted as 1500 μm. The frequency sweep was conducted from 100 to 0.01 rad/s, and all of the rheological measurements were operated at 190 °C.

2.3.4. Mechanical Properties. The single fibers were picked randomly from the multifilaments for mechanical measurement. The fibers were tested via a tensile machine (Zwick Roell Group, Ulm, Germany) equipped with a 10N load cell. The initial length of the gauge was set at 20 mm, and the measurement was conducted at the speed of 20 mm/min, All the measurements were carried out for 10 times, and the average values and the standard deviations were calculated. The measurements were operated under standard atmosphere (the temperature of 20 ± 2 °C, the relative humidity of 65 ± 5%).

2.3.5. DSC Measurement. Differential scanning calorimetry (DSC) measurements were carried out by using Mettler Toledo (Columbus, OH, U.S.A.) to analyze the melting behaviors of the tested fibers. The fibers were first to cut into powder and prepared for DSC measurement. The DSC analysis was conducted from 20 to 220 °C at a heating rate of 10 °C/min in a nitrogen atmosphere. In order to estimate the crystallinity degree (X C ), eq 2 was utilized:

X H H 100 c f f 0 = Δ Δ × (2)
where ΔH f is the heat of fusion of the tested powders (J/g) and ΔH f 0 is the reference value that represents the heat of fusion for a perfect 100% crystalline polymer. For the case of PP, ΔH f 0 can be regarded as 209 J/g. [START_REF] Brandrup | Polymer Handbook[END_REF] 3. RESULTS AND DISCUSSION 3.1. Heat Resistance Properties of Kaolinites. The excellent thermal stability of the nanofillers at the processing temperature is an essential precondition for melt manufacturing. The initial decomposition temperature can give a valuable reference. For both KL and KJ particles, the initial decomposition temperature is around 500 °C at either nitrogen or air atmosphere (Figure 2a,c, respectively). The DTG curves are observable in Figure 2b,d. It is clearly shown that there is even no subtle decomposition below 200 °C. The melt extrusion and spinning were both conducted below 200 °C. The excellent thermal stability of KL and KJ particles ensures that it can be a stable modifier for the polymer blends. These analyses show also a higher weight loss for modified kaolinite which correspond to the degradation of the grafted ODPA. The weight loss when KJ is analyzed under air atmosphere (which corresponds to a total decomposition of the grafted organic part) gives a grafting rate of about 1.4 wt %.

3.2. Morphology of the Nanocomposites. The median grain size (d 50 ) of kaolinite can reach as large as 800 nm, which can provide enough pixels for detection by SEM Figure 3 shows the SEM images of the fractured samples of PP 70 -PVA 30 blends containing the different kaolinites. Combined with the BSE images of the SEM, the localization of the PVA can be It can be observed that the shape of the PVA nodules is governed by continuous structure instead of the spherical structure, which is beneficial for the PVA connectivity in the matrix. [START_REF] Yan | Microstructure Evolution of Immiscible PP-PVA Blends Tuned by Polymer Ratio and Silica Nanoparticles[END_REF] It can be distinguished that unmodified kaolinite KL particles are localized within the PVA phase, while Janusmodified kaolinite KJ particles are distributed at the biphasic interface. It can be concluded that the images show a location of a dominantly more substantial portion of kaolinite at the PP-PVA interface when it is modified.

The PVA accessibility degree of PP 70 -PVA 30 blends with two different kaolinites are illustrated in Figure 4. After the introduction of KL kaolinite nanoparticles, the PVA accessibility is not altered significantly and slightly enhanced by the addition of 5 wt % KL particles. As for the modified kaolinite, the addition leads to the dramatic decrease of PVA accessibility, which is related to the presence of the Janus kaolinite particles at the polymers interface. In our previous research, similar behaviors were investigated. It has been found that the PVA accessibility degrees are tightly relevant to the silica nanoparticles localization within the biphasic polymers, and the accessibility degree of the dispersed phase can even provide a route to predict the localization of the particles. [START_REF] Yan | Microstructure Evolution of Immiscible PP-PVA Blends Tuned by Polymer Ratio and Silica Nanoparticles[END_REF] The filling of the unmodified kaolinite in the dispersed phase may slow down the effect of the destructive process, e.g., fibril breakup, [START_REF] Steinmann | Influence of selective filling on rheological properties and phase inversion of two-phase polymer blends[END_REF] leading to the increment of PVA accessibility. As for the interface-located kaolinite, the effecting function is changed into steric hindrance or surfactants, [START_REF] Zhang | Morphology and rheology of poly(l-lactide)/polystyrene blends filled with silica nanoparticles[END_REF] to prevent the coalescence of PVA phase.

In order to investigate the effect of kaolinite upon the size distribution of PVA nodules of the PP-PVA composites, SEM images of the cross-section of the plates after rheological tests are shown in Figure 5. The volume average diameter of PVA nodules within PP 70 -PVA 30 is 73.3 μm. The PVA nodules perform bimodal distribution, resulting from the annealing of initial spherical and elongated PVA nodules. [START_REF] Parpaite | Janus hybrid silica/polymer nanoparticles as effective compatibilizing agents for polystyrene/polyamide-6 melted blends[END_REF] The initial elongated nodules were relaxed into the spherical PVA nodules with a high diameter value during the rheology test. After a long relaxation time, the deformed droplets evolve to recover the original spherical shapes. [START_REF] Wu | Interfacial Properties, Viscoelasticity, and Thermal Behaviors of Poly(butylene succinate)/Polylactide Blend[END_REF] The characteristic relaxation time of a slightly deformed droplet can be depicted by a concluded formula from Ultracki et al. [START_REF] Luciani | Interfacial tension coefficient from the retraction of ellipsoidal drops[END_REF][START_REF] Kong | Retarded relaxation and breakup of deformed PA6 droplets filled with nanosilica in PS matrix during annealing[END_REF] where p is viscosity ratio of dispersed phase to matrix phase, η m represents the viscosity of the matrix, R 0 represents the radius of a droplet, and σ is the interfacial tension of the blends. Equation 3 indicates that the retraction dynamics depends largely on the viscosity ratio, the interfacial tension, and droplet radius at equilibrium. [START_REF] Wu | Interfacial Properties, Viscoelasticity, and Thermal Behaviors of Poly(butylene succinate)/Polylactide Blend[END_REF] From the SEM observation of the cross sections, the PVA nodules within the blend samples without fillers can be reverted to spherical shapes after the rheology tests, demonstrating a complete relaxation. However, for the KL-containing PP-PVA blends, it cannot be completed changed into spherical shapes and keeps spheroidicity instead. Combined with the localization of KL inside PVA phase, it can be concluded that the increment of the relaxation time is due to the enhancement of the viscosity. What's more, there is also an increment of the size of the PVA nodules with the diameter of 126.1 μm and a dominant fraction of PVA nodules over 170 μm, which also needs a longer relaxation time, based on eq 3. It is worth noting that for the PP 70 -PVA 30 -KL 5 sample, the distribution of PVA nodules is too diverse; thus, the Schwartz-Saltikov transformation cannot be carried out due to the difficulty in statistics. Nevertheless, it can be clearly concluded that the PVA size is further increased with a higher amount of KL.

In contrast, it can be observed that the addition of modified kaolinite KJ leads to the refinement of PVA nodules. When 1 wt % of KJ particles are incorporated, the average diameter of PVA nodules is shifted to a lower value of 51.2 μm and a wide peak appears under 80 μm representing the majority volume fraction. It has been found that the PVA accessibility of KJ- containing extrudates is low, proving the PVA nodules have not formed a highly interconnected structure. In addition, it does not require a long relaxation time because of the small diameter of the PVA droplets. As a result, the PVA droplets also exhibit spherical shapes after the addition of Janus particles. After the addition of 5 wt % of KJ, the size of PVA nodules is significantly reduced and fused into one double peak under 30 μm. The volume average diameter is 13.6 μm for PP 70 -PVA 30 -KJ 5 , presenting only 19% of the diameter of samples without nanoparticles. The refinement effect of KJ particles leads to a higher fraction of isolated PVA nodules in the extrudates, bringing about the drastic decrease of the PVA accessibility degree.

3.3. Rheological Characterization. The rheological results of PP 70 -PVA 30 blends with different kaolinites are displayed in Figure 6a,b. After the introduction of small amount of KL kaolinite particles, there is no distinct difference only leading to a slight enhancement of storage modulus and complex viscosity. For the unmodified kaolinite particles, when the adding amount is increased to 5 wt %, the enhancement effect is not elevated significantly. However, the storage modulus and complex viscosity of the PP 70 -PVA 30 -KJ 5 blend are significantly varied, reflected in the sudden and fierce increment in the low frequency, which proves that a gel-like (yield stress) behavior emerges. In order to demonstrate this phenomenon in depth, the pure polymer component was also tested for the rheological behaviors. PP exhibits a Newtonian plateau of complex viscosity at low shear rates, and instead PVA exhibits a distinctive behavior replaced by a sharp slope, resulting from the existence of dynamic hydrogen bonds between PVA molecules. However, it is worth noting that the storage modulus of the blends is higher than that of the PVA component, although PVA is only a minor phase inside the blends. What's more, the other filler-filling samples have no obvious gel-like behaviors. Related literature [START_REF] Parpaite | Janus hybrid silica/polymer nanoparticles as effective compatibilizing agents for polystyrene/polyamide-6 melted blends[END_REF] revealed that this phenomenon is also attributed to the formation of a solid network of nanofillers (percolation) through the material. Generally speaking, the gel-like phenomenon of the blends originates from the joint effects of the biphasic percolation and hydrogen bonds inside PVA phase.

3.4. Melt Spinning of Nanocomposites. Values of the melt flow indices (MFI) for PP 70 -PVA 30 blends with pristine and modified kaolinites are listed in Figure 7. The MFI values related to the fluidity of the polymer composites are a very significant reference for the determination of melt spinning temperature, among which from 15 to 30 g/10 min the spinning process can be well-optimized. It can be found that the PP 70 -PVA 30 blends perform as the positive correlation compared with the pure components, with an MFI value of 30 ± 1.1 g/10 min (respectively 11.8 ± 0.6 g/10 min for PP and 19.3 ± 0.3 g/10 min for PVA). After the introduction of unmodified KL kaolinite particles, the MFI values are not altered obviously. However, it is not the same case with the incorporation of KJ Janus particles. The KJ particles can effectively decrease the fluidity of the blends (28.1 ± 1.0 g/10 min and 22.2 ± 0.3 g/10 min with 1 wt %/ 5 wt %, respectively) and so increase their viscosity. In the practical spinning, it was found that the temperature profiles had to be adjusted to a higher value to guarantee the success of melt spinning process.

PVA accessibility for PP 70 -PVA 30 fibers with KL and KJ is shown in Figure 8. Although the PVA accessibility of PP-PVA blends is low (lower than 70%), especially for the blends with KJ Janus particles, the PVA accessibility degree can be elevated significantly due to the melt spinning process. It is inspiring that the PVA accessibility degree can always be over 90% for PP 70 -PVA 30 fibers even in the presence of kaolinite particles.

All of the PVA phase can be removed after the selective extraction step, indicating that the PVA nodules are dominantly deformed and interconnected. A highly available PVA makes an excellent contribution to obtain a porous In order to investigate the influence of kaolinite particles upon the mechanical properties of the multifilament yarns, tensile tests were conducted, and the related data are illustrated in Table 1. Moreover, the stress-strain curves are also displayed in Figure 9. The biphasic PP 70 -PVA 30 fibers exhibit good mechanical properties, with the Young's modulus of 1417 ± 136 MPa, tenacity of 11.4 ± 0.6 cN/tex, and elongation at break of 440 ± 39%. With the treatment of selective extraction, the Young's modulus and tenacity keep unchanged, with a significant increment of elongation at break to 632 ± 41%. The related stress-strain curves are displayed in Figure 9a. The shape of the curve for PP is similar to that of selectively extracted PP 70 -PVA 30 -Ex fibers, attributed to the component PP.

Figure 9b,c illustrates the stress-strain curves of the filaments without/with kaolinite particles and before and after selective extraction. After the introduction of KL particles, the mechanical properties are gradually weakened. The tenacity and breaking elongation are decreased, and Young's modulus is kept stable. After the introduction of 1 wt % of KJ particles into the blends, the tenacity and breaking elongation is decreased slightly and value of the Young's modulus is slightly increased. However, if 5 wt % of KJ is introduced, the Young's modulus is increased fiercely from 1417 ± 136 MPa for unfilled blends toward 2227 ± 370 MPa, with a sharp 57% increment. Meanwhile, its tenacity is slightly enhanced with a decrease of elongation at break. It proves that the introduction of the Janus particles significantly enhances the Young's modulus of the fibers, with a strengthening of the interface due to the particular localization of the kaolinite particles. The decrease in the breaking elongation also gives the evidence of the interfacial localization of the fillers. So Janus-modified particles can also be utilized in melt spinning technology fields, to offer a remedy and enhancement strategy for the weakening effect in mechanical properties of melt-spun fibers.

Meanwhile, after selective extraction, the breaking elongation is increased due to the removal of PVA. The tenacity has no significant change after the extraction. The Young's modulus is decreased at a different degree, and that of PP 70 -PVA 30 -KJ 5 is recovered near that of the filaments without fillers. It hints that the great mechanical contribution of the filaments with Janus particles mainly originates from PVA phase.

Melting points of PP and PVA are very close, which complicates the analysis of their crystallization degree. However, it is still of great interest to dig the related information on the PP porous fibers obtained after PVA extraction. Therefore, the DSC spectra of PP and PP 70 -PVA 30 -Exfibers (after selective extraction) are shown in Figure 10a, with data details in Table 2. Figure 10a shows that there is one peak around 166.5 °C for neat PP fibers. For PP 70 -PVA 30 -Ex fibers, the shape of the DSC spectra is similar to the adjacent melting point of 166.3 °C, while the crystallinity degree is decreased. It is one of the reasons that the mechanical properties of PP 70 -PVA 30 -Ex fibers are weaker. It is also due to the generated porous structures forming tremendous stress concentration points. To investigate the influence of kaolinite particles to the crystallization of PP porous fibers, the DSC spectra of the composite fibers after extraction are shown in Figure 10b with details in Table 2 as well. The PP 70 -PVA 30 -Ex fibers show a single melting peak during heating, and the crystallization degree is not altered obviously after the addition of the clay. However, fibers obtained using kaolinite exhibit two endothermic maxima instead of a single peak. Compared with unmodified kaolinite, the impact of Janus particles to the DSC peak splitting is more significant. The peak splitting is associated with a degree of perfection of the crystals, of which the high-temperature endotherm is caused by the recrystallization due to more thermal energy. [START_REF] Cho | Structural properties and superhydrophobicity of electrospun polypropylene fibers from solution and melt[END_REF][START_REF] Torre | Melting behavior in blends of isotactic polypropylene and a liquid crystalline polymer[END_REF] It indicates that the perfection of the crystals is enhanced by the addition of the kaolinite particles, especially the Janus-modified ones.

CONCLUSION

We originally introduce Janus particles into melt spinning systems, with hybrid kaolinite as an example. The particles localize at the PP-PVA interface and significantly enhance the polymer compatibility. The melt-spun yarns from PP 70 -PVA 30 are mechanically improved, of which the Young's modulus is significantly increased. Meanwhile, the Janus modified kaolinite influences the morphology of the extrudates as well, reflected in the differentiated PVA accessibility. It also has obvious impact upon the rheological behavior. Indeed, rheological characterizations show a gel-like behavior for 5 wt % of KJ, originating from the PVA and from a percolation effect due to the interfacial localization of the Janus particles. The KJcontaining samples also exhibit lower melt flow indices. Furthermore, the PP crystallization rate decreases with the addition of the kaolinite particles even if the perfection of the crystals is improved.

Notes

The authors declare no competing financial interest.

■ REFERENCES

(

Figure 1 .

 1 Figure 1. Schematic representation of (a) the synthesis of Janus kaolinite particles; (b) the manufacturing process of surface-modified porous PP fibers.

Figure 2 .

 2 Figure 2. TG and DTG curves of KL and KJ particles in nitrogen and air atmosphere. (a) TG curves in a nitrogen atmosphere; (b) DTG curves in a nitrogen atmosphere; (c) TG curves in an air atmosphere; (d) DTG curves in an air atmosphere.

Figure 3 .

 3 Figure 3. SEM images of PP 70 -PVA 30 blends with pristine and modified kaolinites (samples were cryo-fractured after treatment in liquid nitrogen). (a) PP 70 -PVA 30 -KL 5 ; (b) PP 70 -PVA 30 -KL 5 (BSE); (c) PP 70 -PVA 30 -KJ 5 ; (d) PP 70 -PVA 30 -KJ 5 (BSE).

Figure 4 .

 4 Figure 4. PVA accessibility degree of PP 70 -PVA 30 extrudates with pristine and modified kaolinites at 1 or 5 wt %.

Figure 5 .

 5 Figure 5. Particle diameter distribution and the SEM images of the PVA nodules inside PP matrix after the rheometer measurement (a) PP 70 -PVA 30 , (b) PP 70 -PVA 30 -KL 1 , (c) PP 70 -PVA 30 -KJ 1 , (d) PP 70 -PVA 30 -KJ 5 .

Figure 6 .

 6 Figure 6. Rheological results of PP 70 -PVA 30 blends and pure components (a and b) storage moduli and complex viscosities of PP 70 -PVA 30 blends without/with kaolinite particles (c and d) storage moduli and complex viscosities of PP, PVA, PP 70 -PVA 30 blends without/with 5 wt % of KJ Janus particles.

Figure 7 .

 7 Figure 7. Melt flow index of PP 70 -PVA 30 blends with different kaolinites at 190 °C.

Figure 8 .

 8 Figure 8. PVA accessibility of PP 70 -PVA 30 fibers with pristine and modified kaolinites.

Figure 9 .

 9 Figure 9. Stress-strain curves of PP 70 -PVA 30 fibers with pristine and modified kaolinite particles (a) before extraction; (b) after extraction; (c) neat PP fibers.

  mail: xiang.yan@ensait.fr. Tel.: + 33(0)3.20.25.86.90. ORCID Xiang Yan: 0000-0002-7985-4249

Figure 10 .

 10 Figure 10. DSC spectra of (a) neat PP and PP 70 -PVA 30 -Ex and (b) PP 70 -PVA 30 without/with kaolinite particles after selective extraction.

Table 1 .

 1 Mechanical Properties of PP 70 -PVA 30 Fibers Containing Kaolinite Particles and Neat PP Fibers

			before extraction			after extraction
	fibers	Young's modulus (MPa)	tenacity (cN/tex)	elongation at break (%)	Young's modulus (MPa)	tenacity (cN/tex)	elongation at break (%)
	PP 70 -PVA 30	1417 ± 136	11.4 ± 0.6	440 ± 39	1453 ± 316	12.2 ± 2.5	632 ± 41
	PP 70 -PVA 30 -KL 1	1401 ± 96	10.2 ± 0.7	421 ± 40	1248 ± 195	11.2 ± 1.7	503 ± 54
	PP 70 -PVA 30 -KL 5	1485 ± 248	9.5 ± 1.7	327 ± 74	1189 ± 294	8.4 ± 2.3	428 ± 76
	PP 70 -PVA 30 -KJ 1	1657 ± 303	10.2 ± 1.0	408 ± 21	1413 ± 290	11.8 ± 2.0	604 ± 52
	PP 70 -PVA 30 -KJ 5	2227 ± 370	12.2 ± 1.6	299 ± 82	1447 ± 395	11.7 ± 3.4	488 ± 80
	PP	2174 ± 238	17.9 ± 2.2	516 ± 66

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Table 2 .

 2 Thermal Properties of Neat PP and PP 70 -PVA 30 Fibers after Extraction

	PP	83.34	39.9	166.5
	PP 70 -PVA 30 -Ex	75.60	36.2	166.3
	PP 70 -PVA 30 -KL 1 -Ex	72.77	34.8	166.8
	PP 70 -PVA 30 -KL 5 -Ex	71.69	34.3	164.9	170.2
	PP 70 -PVA 30 -KJ 1 -Ex	80.26	38.4	165.5	170.4
	PP 70 -PVA 30 -KJ 5 -Ex	72.67	34.8	165.5	170.3

enthalpy (J/g) X c (%) T peak1 (°C) T peak2 (°C)