Huiqin Chen
email: huiqin.chen@u-psud.fr

Emanuel Aldea
email: emanuel.aldea@u-psud.fr

Sylvie Le Hégarat-Mascle

Integrating Visual and Geometric Consistency for Pose Estimation

In this work, we tackle the problem of estimating the relative pose between two cameras in urban environments in the presence of additional information provided by low quality localization and orientation sensors. An M-estimator based approach provides an elegant solution for the fusion between inertial and vision data, but it is sensitive to the prior importance of the visual matches between the two views. In addition to using cues extracted from local visual similarity, we propose to rely at the same time on learned associations provided by the global geometrical coherence. A conservative weighting scheme for combining the two types of cues has been proposed and validated successfully on an urban dataset.

Introduction

This paper aims at exploiting and improving the fusion of pose priors from sensors (GPS, accelerometers, gyroscopes, magnetometers) and image features for the estimation of relative camera pose. The estimation of relative camera pose is a fundamental step for many computer vision tasks, such as structure from motion(SFM), SLAM or object tracking. Traditional pose estimation relies on image features: given a pair of images (I a , I b ), the standard procedure may be described as follows:

• Extract SIFT keypoints for (I a , I b ) and assign a built-in descriptor to each keypoint;

• Construct keypoint correspondences from I a to I b by matching them using SIFT descriptor distance minimization with a rejection threshold based on the ratio of the two smallest distances;

• Calculate the relative pose (R, t) by decomposing the fundamental or essential matrix after having computed it using a robust estimation RANSAC on correspondences, or estimate (R, t) directly using an iterative estimation method such as an Mestimator.

However, this traditional strategy only based on image feature is limited by inaccurate matching due to the presence of repetitive features and occlusions, which often occur in urban scenes. Nowadays, sensors available in GPS receivers or IMUs are widely integrated with camera devices in smart phones. These sensors provide a readily available camera pose information, albeit often very imprecise. Despite its uncertainty, the sensor prior can play a crucial role in improving pose estimation based on image feature.

There exist some fundamental approaches for integrating the pose prior with image information. The authors of [START_REF] Dr Wong | IMU-aided SURF feature matching for relative pose estimation[END_REF] fuse the pose prior provided by IMU during the matching step in a process which can be considered as filtering for the relative pose estimation. They constrain the search area for correspondences around the epipolar line defined by sensor data in order to guide the matching. As mentioned in [START_REF] Goldman | Robust epipolar geometry estimation using noisy pose priors[END_REF], this method is very sensitive to sensor noise.

For many works on visual inertial SLAM using temporal sequences, a common fusion method is to constrain separately the image based estimation and pose prior as prediction step and correction step for Kalman filtering [START_REF] Pollefeys | Detailed real-time urban 3d reconstruction from video[END_REF][START_REF] Brockers | Fully self-contained vision-aided navigation and landing of a micro air vehicle independent from external sensor inputs[END_REF]. Provided that video sequences cover in detail the area of interest, even pure visual SLAM may provide accurate results for registering cameras in urban scenes [START_REF] Pollok | A visual SLAM-based approach for calibration of distributed camera networks[END_REF]. However, in our work we study the more constrained scenario in which a single pair of images is available, along with a low quality pose prior provided by low cost GPS and inertial sensors. Indeed, in metropolitan areas video-recording (especially using UAVs) is highly regulated, and even if videos from ground-level dynamic cameras are available, they have often low quality and are heavily occluded.

In the last years, minimizing a loss function including both image features and pose prior by non linear optimization has been shown to be more accurate than Kalman filtering in visual-inertial SLAM [START_REF] Leutenegger | Keyframebased visual-inertial odometry using nonlinear optimization[END_REF]. To the best of our knowledge, [START_REF] Goldman | Robust epipolar geometry estimation using noisy pose priors[END_REF] is the first work who extends a similar idea for the relative camera pose estimation for a single pair of views. Instead of using RANSAC, they propose an algorithm called SOREPP which relies on the fusion of putative correspondences and of noisy pose priors from sensors. In order to be robust to outliers, they use the following M-estimator:

ŝ = arg min s c k∈Ω w(k)(1 -g(k, s)) + λ(s) 2 , ( 1
)
where c is a weighting parameter, Ω is the set of putative correspondences and w(•) their weights, g(k, s) is a Gaussian score evaluated for the correspondence k with respect to the relative pose s, and the regularization term λ(s) is a distance measure between s and the sensor prior.

The performance of SOREPP depends on the sensor precision as well as on the quality of correspondences in the set Ω and the values of w(•). If the GPS/IMU uncertainty is too high, the regularization term value λ(s) decreases and impacts less the optimization. The presence of a significant ratio of outliers in Ω makes it difficult for the M-estimator to find good solutions, except with very precise pose priors. In order to guide the M-estimator, SOREPP estimates a correspondence weight in the form of a rough approximation of the prior probability of being an inlier [START_REF] Goldman | Robust epipolar geometry estimation using noisy pose priors[END_REF] (see Section 2.1).

Although it has the advantage of being simple, the proposed weighting tends to be unreliable in scenes with repetitive patterns such as urban contexts.

Our work aims to further improve the estimation of weights for correspondences, and therefore to improve the performance of pose estimation based on SOREPP. To this aim, we propose that weights be also based on a global geometry consistency estimated by a deep network recently introduced in [START_REF] Yi | Learning to Find Good Correspondences[END_REF]. Then, we explore how to combine this additional criterion with the weighting strategy based on local classical features in SOREPP. Our main contributions consist in:

• applying jointly a neural network with a pose prior for robust relative camera pose estimation;

• developing an effective correspondence weighting strategy by exploiting SIFT features as well as geometric consistency.

Proposed estimation

In the following, we discuss the weighting strategy based on local visual features from [START_REF] Goldman | Robust epipolar geometry estimation using noisy pose priors[END_REF], as well as on global geometry consistency as introduced recently in [START_REF] Yi | Learning to Find Good Correspondences[END_REF]. The last part of this section highlights how these two weighting strategies guided by fundamentally different objectives may be used jointly in order to obtain robust correspondence weights.

Weights from classical features

A set of putative correspondences Ω is constructed using the classical SIFT nearest neighbor strategy [START_REF] Dg Lowe | Distinctive image features from scale invariant keypoints[END_REF] with a standard ratio threshold of 0.75. SOREPP defines independently the weight w(k) for each correspondence k based on the SIFT ratio test [START_REF] Dg Lowe | Distinctive image features from scale invariant keypoints[END_REF] :

∀k ∈ Ω, w(k) = 1 - d 1N N (k) d 2N N (k) . (2)
The underlying assumption is that more distinctive the first nearest neighbor, more likely the matching corresponds to an inlier. However, this cue which is based only on the descriptor appearance, is not robust in presence of repetitive features and occlusion. Furthermore, while decreasing the number of outliers, the nonadaptive ratio threshold reduces as well the number of inliers, an outcome which is undesirable when inliers are scarce. Next we will discuss the weighting strategy based on global geometry consistency which avoids relying on a fixed ratio threshold. Given all coordinates of putative correspondences in Ω = {k 1 , k 2 , ..., k i , ..., k N }, with k i = (x ai , y ai , x bi , y bi ) ∈ Ω, for an image pair (I a , I b ), which is constructed by classical SIFT nearest neighbor strategy without ratio threshold, a weakly supervised deep network is trained in [START_REF] Yi | Learning to Find Good Correspondences[END_REF] to estimate a global geometrically consistent weight w k ∈ [0, 1] for each correspondence:

Weights from the geometry network

{w(k 1 ), w(k 2 ), ..., w(k i ), ..., w(k N )} = f Φ (Ω), (3)
where f Φ (•) is the regression of a deep network with parameters Φ. If w(k i ) = 0, the correspondence k i is likely to be an outlier and classified as such, following the precautionary principle.

The core idea is that inliers are geometrically structured, conversely to outliers. Unlike the weight based on ratio test in Section 2.1 that depends only on the considered correspondence, when considering geometric consistency all correspondences contribute to the estimation of w(k i ). Because this globally geometric weight is independent of appearance, it can potentially avoid the appearance ambiguity for repetitive features.

Exploiting both cues

It is useful to exploit jointly the local appearance cue and global geometric consistency cue because of their complementary information and their relative balance in performance and precision. In the following part, different combinations of these cues are investigated and compared. For the sake of simplicity, w v denotes the weight from classical visual features given by Eq. ( 2), w g the weight from the geometry network given by Eq. ( 3) and w (i) the combined weight to be investigated for a specific correspondence k.

Linear operation. The simplest fusion approach is to consider the average of w g and w v :

w (1) = w g + w v 2 , (4)
Averaging can be seen as a weight filtering which allows to keep considering correspondences having possible a very low weight w v or w g , while reinforcing correspondences with both high weights. In our case, this averaging approach is taken as the baseline.

Regression. Instead of applying a simple averaging operation, it is possible to learn automatically the fusion weight by logistic regression :

w (2) = g(w g , w v ), (5)
where g(•) represents the bivariate logistic regression function.

In order to train the regression model, we choose image pairs in the dataset of [START_REF] Yi | Learning to Find Good Correspondences[END_REF] and match corners using the classical nearest neighbor SIFT strategy. The values w v are computed during the matching step, whereas values w g are obtained by taking the matched correspondence coordinates for each image pair as the input of neural network model in [START_REF] Yi | Learning to Find Good Correspondences[END_REF] with the trained model. By choosing a fixed Sampson distance threshold of matching points to the epipolar line, each correspondence is labeled as 1 for inliers or 0 for outliers. As expected, in our setting the resulting weight is close to 1 when w v is larger than 0.8 irrespective of the value of w g . This seems to be reasonable since for few outliers the ratio test is higher than 0.8 according to Fig. 1, upper histogram. Compared to averaging, regression tends overall to increase the combined weight, which might increase more the influence of potential inliers but also that of some outliers.

Conservative weighting. The previous strategies introduce more or less some confusion between inliers and outliers in terms of weighting for the M-estimator based optimization. In order to be stricter, we propose to adopt a conservative weighting based on a pessimistic function such as min(w g , w v ). One significant consequence is that, in contrast to previous strategies, the min operation discards entirely the correspondences with w g = 0, which exhibit a high ratio of outliers.

Since w v seems to be more unreliable according to Fig. 1, the symmetric weight from visual cues denoted by w vs (computed by interchanging for the association the source and destination images in the pair) is also taken into account in order to constrain more the influence of visual part, and w v is substituted by min {w v , w vs }. However, the geometry network exhibits already a conservative behaviour in the way it outputs the weights (see Fig. 1, lower histogram), thus the symmetric weight from the network denoted by w gs is used conversely in order to allow more prospective points identified by geometric coherence: max {w g , w gs }. Finally, by taking into account the particular behaviour of the two weighting algorithms, the fusion weight is computed by the following formula:

w (3) = min {max {w g , w gs } , min {w v , w vs }} , (6)
The result in Eq. ( 6) is also due to the fact that the loss function used in the neural network encourages a crisp decision regarding the nature of each input observation, a behavior which is clearly visible as well in Fig. 1 when comparing the geometry weighting with the histogram of w v values.

Experiments

In the experimental part, we evaluate the performance of the weighting strategies introduced in the previous section, and we compare them to SOREPP algorithm [START_REF] Goldman | Robust epipolar geometry estimation using noisy pose priors[END_REF] and to the geometric network [START_REF] Yi | Learning to Find Good Correspondences[END_REF] on a dataset collected in a challenging urban environment. We start by presenting the dataset, then we introduce the implementation details followed by the evaluation metric. The quantitative details are then discussed.

Dataset

We collected an urban scene dataset containing 32 images acquired with a smart phone in front of a major railway station, some of the images being taken at ground level and some from the upper floors of a building. As in [START_REF] Goldman | Robust epipolar geometry estimation using noisy pose priors[END_REF], the GeoCam application was used to record in each image header the approximate pose provided by the embedded sensors. The ground truth was constructed by feeding all the images into Visu-alSFM [START_REF] Wu | VisualSFM: A visual structure from motion system[END_REF]. The relative poses were varied gradually in order to ensure a high-quality VisualSFM estimation, and also to provide image pairs with varying degrees of difficulty for our pose estimation problem. The dataset is provided to the academic community1 .

Implementation

Given an image pair from the dataset, we compute two correspondence sets Ω 1 and Ω 2 using the SIFT nearest neighbor strategy with different ratio test thresholds. A standard ratio test threshold 0.75 is chosen for Ω 1 . For Ω 2 , we choose a very high value 0.95 which almost amounts to cancelling the ratio test, but it actually still helps eliminating a fair number of correspondences. We compute the weight w v as in Eq.

(3) for each correspondence in Ω 1 and Ω 2 during the matching step. Furthermore, we take all normalized matching point coordinates in Ω 2 as the input of the geometric network in [START_REF] Yi | Learning to Find Good Correspondences[END_REF] and we get a geometric weight w g for each correspondence in Ω 2 . We use Ω * 2 to denote the subset of Ω 2 where w g is greater than 0 and each correspondence in Ω 2 is normalized. We applied the model trained by the authors of [START_REF] Yi | Learning to Find Good Correspondences[END_REF] on outdoor scenes. At the same time, we also compute three versions of fusion weight w (1) , w (2) and w (3) for Ω 2 separately with Eq. ( 4), ( 5) and [START_REF] Dg Lowe | Distinctive image features from scale invariant keypoints[END_REF].

According to the various inputs and algorithms, we classify the different estimation methods as shown in Tab 1. SOREPP Ω 2 , w (1) , sensor Ours-REG SOREPP Ω 2 , w (2) , sensor Ours-CNSV SOREPP Ω 2 , w (3) , sensor

Evaluation metric

Given the estimated rotation matrix and translation vector (R es , t es ) and the ground truth (R gd , t gd ) for relative camera poses, we compute the rotation error δR and translation error δt separately for the evaluation. For δR, we firstly compute the relative rotation matrix ∆R between R es and R gd , with ∆R = R T es • R gd . ∆R can be represented by a rotation of angle φ around a vector v. Smaller is φ, closer R es is to R gd . Thus the extent of the rotation error δR can be approximated by φ which is computed as (see page 584 in [START_REF] Hartley | Multiple view geometry in computer vision[END_REF]):

δR ≈ φ = arccos Tr(∆R) -1 2 , (7)
with T r(•) the trace of a matrix. The translation error δt is computed as the angle between t es and t gd :

δt = arccos t es • t gd t es t gd , (8)
In the following, we take the pose estimation error as the maximum value between δR and δt. To evaluate the performance of each method, we compute the cumulative curve of pose estimation error taken, as previously performed in [START_REF] Yi | Learning to Find Good Correspondences[END_REF] and in [START_REF] Jw Bian | Gms: Grid-based motion statistics for fast, ultra-robust feature correspondence[END_REF]. For each method, the corresponding curve presents the percentage of image pairs whose pose is successfully estimated with respect to a given error threshold. As a qualitative result, we illustrate in Fig. 2 a relative pose estimation for an image pair, and the corresponding epipolar lines and epipole locations for the different methods considered.

Quantitative evaluation

The evaluation results are presented in Fig. 3. When comparing the weighting strategies and the other methods, the Ours-CNSV approach which uses the conservative fusion rule shows the best performance. The important role of the noisy pose priors provided by sensors is confirmed by the SOREPP based estimation (SIFT-SOREPP) which improves over the pure vision based algorithms, namely classical SIFT-RANSAC and the geometry based network (GEO-RANSAC). The linear weighting and the bivariate regression weights are less effective in supporting the M-estimator, especially in the high precision range (error smaller than 5 degrees) which is desirable for the accurate localization of elements of interest in the camera fields of view. Our experiments confirm systematically that relying on the more permissive Ω 2 threshold for visual filtering is more effective than using the stricter Ω 1 , because the global geometric consistency provided by the neural network identifies more reliably the inliers than the local fixed ratio test threshold used classically for SIFT matching.

Conclusion

For the relative pose estimation problem, we proposed a strategy to rely at the same time on local information provided by visual similarity, and by a global geometrical coherence of the transformation between two views. We combined the two types of cues along with localization and orientation information within an M-estimator, with excellent results on real data acquired in an urban scenario.

In future work, we intend to formalize the usage of prior knowledge in the combination process in order to extend it for other sources of information assessing the reliability of interest point matches, i.e. belonging to the same semantic category or exhibiting local similarity in terms of a learned function. The ground truth (Fig. 2b) is computed using SfM on a larger set of images. The quality may be assessed visually based on the position of the manually defined control points with respect to their epilines, and based on the location of the epipole.

Figure 2 :

 2 Figure2: Illustration of pose estimation results between a ground level view (Fig.2a) and an overview camera. The ground truth (Fig.2b) is computed using SfM on a larger set of images. The quality may be assessed visually based on the position of the manually defined control points with respect to their epilines, and based on the location of the epipole.

Figure 3 :

 3 Figure 3: Rotation and translation success ratio of various pose estimation algorithms depending on a given error threshold.

Table 1 :

 1 Estimation method

	Name	Algorithm	Input
	Sensor	-	-
	SIFT-RANSAC GEO-RANSAC [5] RANSAC RANSAC	Ω 1 Ω * 2
	SIFT-SOREPP [2] SOREPP	Ω 1 , w v , sensor
	GEO-SOREPP	SOREPP	Ω 2 , w g , sensor
	Ours-LO

The data used in this work may be found at: http:// hebergement.u-psud.fr/emi/S2UCRE/ChenMVA19.zip

Acknowledgements

This work was funded by the French National Research Agency grant ANR-16-SEBM-0001 (the "S 2 UCRE" project) within the French-German funding framework "Safety and Security of Urban Crowded Environments".


K Rachedi
email: kammel.rachedi@espci.fr

D.-T Phan-Huy

N Selmene

A Ourir

Matthieu Gautier

A Gati

R Fara

Julien De Rosny

A Galindo-Serrano

la diffusion de documents scientifiques de niveau

Keywords:

recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

I. INTRODUCTION

The population of connected objects or Internet-of-Things (IoT) is dramatically expanding and raises several challenges. First, a large number of connected objects must be selfpowered. Secondly, though connected objects use very low data rate they still need to be spectral efficient as the spectrum resource is scarce. To overcome these two issues, a few years ago, Liu et al. [START_REF] Liu | Ambient backscatter: wireless communication out of thin air[END_REF] proposed a new communication system, called ambient backscatter, inspired by Stockman in 1948 [START_REF] Stockman | Communication by Means of Reflected Power[END_REF] and Radio Frequency Identification (RFID) systems. In a classical communication, the transmitter emits and modulates an RF wave that is detected by a receiver. In the system proposed by Liu et al. [START_REF] Liu | Ambient backscatter: wireless communication out of thin air[END_REF], the transmitter (called tag) communicates with the receiver (called reader) without emitting any RF wave. An RF source of waves (such as TV broadcast) illuminates both the tag and the reader. The tag transmits information to the reader by electronically changing its own reflectivity at the symbol rate. In practice, it switches a metallic wire to make it resonating (or not) at the frequency of the source. By doing so, the tag modifies the level of scattering in the radio propagation channel between the reader and the source. To recover the transmitted message, the reader detects these fluctuations in the channel power. The RF tag communication circuit is ultra low power and can be powered by an RF energy harvesting circuit. In order to assess the performance of this system in terms of Bit Error Rate (BER), as a function of time, relative locations of the three elements, types of sources (TV, Wi-Fi and 4G) or propagation, we have developed small and flexible prototypes of tags and readers, communicating in real time.

Thanks to this versatile material setup, we show and demonstrate experimentally, in real time, that even when the tag, the source and the reader are in Line of Sight (LoS), the performance fades for some spatial relative configurations of the three elements. We also make real-time demonstrations with various sources.

II. BACKSCATTER AND READER PROTOTYPES

We have developed versatile prototypes of backscatter communication. For the purpose of live demonstrations, a tag based on MSP430 micro-controller either powered by a lithium battery or more by a solar cell (see Fig. 1a and[3]) has been built. The tag switches every 2.7 ms and sends a Frequency Modulation 0 (FM0) symbol every 5.4 ms (with a data rate of 185 bits/s). The tag loop-transmits 4 successive images of 88 bits encoded in FM0 (11x8 black and white pixels) of sprites (space invaders) and a 8-bit synchronisation sequence. The reader consists of a NooElec NESDR mini connected to a laptop or a Raspberry PI3, that runs an energy level comparator implemented under GNU Radio [START_REF] Blossom | Gnu radio: tools for exploring the radio frequency spectrum[END_REF], an open-source software defined radio (SDR). Thanks to this transportable set-up (not connected to the eletric network), the backscatter communication has been tested in various environments such as high speed train (see Fig. 1c and[ To study the performance of the backscatter communication system with respect to the relative positions of the three elements, we have developed another tag with an ATmega328 -Arduino, and another reader consisting of an USRP-B210 connected to a laptop, still using the energy detector.

III. SPATIAL PERFORMANCE

As metrics, we use the BER and we introduce the relative power contrast (PC) that is equivalent to the modulation index for classical amplitude modulation. The PC directly impact the BER. Indeed, BER = 0.5erfc(𝜂/𝜁) where 𝜂 is the PC and 𝜁 the signal to noise ratio. To scan both BER and PC, the tag is mounted on two translation stepping motors (see Fig. 2). Moreover, to control the position of the source, the ambient source is replaced by a controlled dipole generating RF noise connected to a USRP-B210. To study a configuration near to line-of-sight, some absorbing elements can be placed around the setup in order to mitigate reflections. In Fig. 3a the distribution of BER is plotted for different positions of the tag. The BER is assessed from the transmission of 2000 bits. Despite this LoS configuration, we observe strong fluctuations and fades of the BER. Note that the simple BER model based on PC fits very well the experimental BER (see inner plot in Fig. 3a). In order to have a better knowledge of the BER fading patterns, three 2D-scans of PC have been performed at 3 different working frequencies (see Figs. 3b-d): corresponding to Wi-Fi (2.4 GHz and 5.2 GHz) and future 5G (3.5 GHz). To interpret this results, we propose a simple scattering model. The field recorded by the reader is the superposition of the direct wave between the source and the reader and the wave that is backscattered. The complex field amplitude 𝑉 𝑖 𝑟 is given by

𝑉 𝑖 𝑟 ∝ 1 𝑑 𝑒 𝑗𝑘0𝑑 + 𝑓 𝑖 𝑑 1 𝑑 2 𝑒 𝑗𝑘0(𝑑1+𝑑2) ,
where 𝑖 is either equal to 0 or 1 depending on the switch state. Distances 𝑑, 𝑑 1 , 𝑑 2 are defined on Fig. 2. The complex numbers 𝑓 0 and 𝑓 1 are the scattering amplitudes of the resonator for the 2 states of the tag. For a resonant scatterer that is switched on [START_REF] Liu | Ambient backscatter: wireless communication out of thin air[END_REF] or off (0), |𝑓 0 | < |𝑓 1 |. From the previous equation, we note that the field at the reader location is constant when 𝑑 1 + 𝑑 2 is constant. As a consequence, PC patterns describes ellipsis where the foci are the positions of the source and the reader. We observe on Figs. 3b-d that the maxima and minima of PC are very well described by these ellipses. To overcome this strong fading effect, we are currently developing more advanced detection algorithms.

IV. LIVE DEMONSTRATIONS

We perform two live demonstrations using the versatile prototypes described in Sections II and III: 1) a real-time looptransmission of 8 sprites with different sources (in TV or 4G bands) and a real-time spatial evaluation of the BER and PC metrics, with a tag moving along one axis.

V. ACKNOWLEDGMENT

  5]), underground , or indoor [6] (with TV tag-reader distance of around 5 meters) and for different sources, such as TV (500-700 MHz), Wi-Fi (2.4 GHz) and 4G downlink bands (778-788, 791-801, 801-811 and 811-820 MHz) (see Fig.1b and [7]).

Figure 1 .

 1 Figure 1. Photos of (a) Sun-powered TV backscatter (b) 4G backscatter (c) TV backscatter on board of a Paris-to-Lyon high speed train,

Figure 2 .

 2 Figure 2. Configuration to measure the spatial dependancy of PC and BER with respect to the relative position of the source, reader and tag. The BER and PC shown on Fig. 3a have been measured on the blue dash-dotted line. The PC shown in Figs. 3b-d have been recorded in the 30cmx30cm squared area.

  Figure 3. (a) Distribution of BER at 5.2 GHz when the tag is moved on a linear trajectory perpendicular to the line connecting the emitter and reader (see Fig. 2). Inner plot is the BER vs BER = 0.5erfc(𝜂/𝜁) (see text). Two dimensional scans of PC (dB scale) at 2.4 GHz (b), 3.5 GHz(c) and 5.2 GHz(d). The scanned squared area is shown on Fig. 2. The red lines are ellipsis where the foci are the locations of the emitter and reader.

We warmly thank T. Sarrebourse and W. Diego for their help. This work is partially supported by the French project SpatialModulation under grant number ANR-15-CE25-0016.


E Uitterhaegen

L Labonne

S Ballas

T Véronèse

Ph Evon

Keywords: Coriander straw, lignocellulosic fibers, twin-screw extrusion refining, compression molding, building insulation materials

Straw represents 60-80% of the aerial part of coriander. It is cheap (90 bunching and transportation) and has an availability of 250 tons/year. However, the latter will grow strongly in the next five years due to the increasing use of vegetable oil from the fruits for food, cosmetics or the chemical industry. Due to its high lignocellulose content (62%), coriander straw is an interesting crop by-product for the production of bio-based building materials. Two types of insulating materials are presented here. Firstly, it is possible to produce bulk materials by thermo-mechano-chemical refining of straw with water using a twin-screw reactor. According to the applied liquid/solid ratio (0.4-1.0), it is possible to control the fiber aspect ratio of the refined straw (22.9-26.5) and thus the tapped density of the resulting bulk material (110-61 kg/m 3 ). For the lowest density, the thermal conductivity was 47.3 mW/(m.K), corresponding to a 1.06 (m 2 .K)/W resistance for a 5 cm thickness of extrusion-refined straw. Twin-screw refining was also successfully conducted with an aqueous borax solution, allowing fire-proofing of the straw. When used as loose fill in housing, refined coriander straw is a promising solution for building insulation. Medium-density insulation blocks can also be manufactured using compression molding by combining coriander straw (milled or extrusion-refined) and a starch-based binder. The use of a milled straw (7.5 mm sieve) mixed with 15% binder, cold pressed (87 kPa, 30 s) and then dried, resulted in cohesive blocks with a 155 kg/m 3 density and a 55.6 mW/(m.K) thermal conductivity, corresponding to a 0.90 (m 2 .K)/W resistance for a 5 cm thickness. Similarly, such blocks could be used for the thermal insulation of buildings, including the filling of walls, interior partitions, etc.

INTRODUCTION

Coriander (Coriandrum sativum L.) is an annual herb, commonly used as a condiment or a spice. The fruit has been used as a traditional medicine to treat various medical conditions such as indigestion, worms, rheumatism and joint pain. It also contains a vegetable oil which can be extracted through mechanical pressing using a twin-screw extruder [Uitterhaegen 2015]. Pressed oils are of good quality (<1.5% acidity and negligible peroxide value) and show a high petroselinic acid content (73%). They are also pleasantly scented, owing to part of the coriander essential oil being coextracted from the fruits during the extraction process. Straw represents 60-80% of the aerial part of coriander and is considered a crop residue, rendering it economically interesting with a low price of including harvesting, bunching and transportation. Nowadays, its availability is 250 tons/year in the South-West part of France. However, it will grow strongly in the next five years, since vegetable oil from coriander fruits has gained great interest from the food, cosmetic and chemical industries [Uitterhaegen 2016]. Coriander straw was recently used as a reinforcing filler in polypropylene (PP) and bio-based low-density polyethylene (BioPE) composite materials through twinscrew extrusion compounding and injection molding [Uitterhaegen 2018]. Its reinforcing effect was significant, resulting in a 50% increase in the flexural and tensile strengths (from 19 to 28 MPa and from 12 to 17 MPa, respectively, for polypropylene composites) as compared to the native polymer. From a mechanical point of view, the 40% filled thermoplastic biocomposite had comparable properties to those of commercial wood fibers. In addition, its durability was excellent in terms of UV and hygrothermal weathering. Lastly, it revealed high potential for recycling. Coriander straw has also already been successfully used as a mechanical reinforcement inside renewable binderless fiberboards molded using hot pressing [Uitterhaegen 2016]. The protein-based press cake resulting from the extraction of vegetable oil from the fruits was then used as a natural binder inside the boards, thus at the same time ensuring the valorization of a crop residue and a process by-product. The resulting fiberboard revealed good performance, i.e. a flexural strength of 29 MPa and a thickness swelling of 24%. Produced without the use of any chemical adhesives, such board could thus present a viable, sustainable alternative for current commercial woodbased materials such as oriented strand board (OSB), particleboard (PB) and medium-density fiberboard (MDF), with high cost-effectiveness. Another industrial application of fiberboards made from crop residues is heat insulation of buildings (e.g. walls, ceilings, etc.), where the main advantages of vegetable fibers are their abundance, low cost, minimal environmental impact, independence from fossil resources, and their natural capacity for thermal insulation [START_REF] Saiah | [END_REF]]. Many different agricultural byproducts were used in the two last decades for obtaining innovative and renewable insulation boards, e.g. a mixture of durian peels and coconut coir fibers [Khedari 2003], kenaf fibers [Ardente 2008], flax and hemp fibers [START_REF] Kymäläinen | Flax and hemp fibres as raw materials for thermal insulations[END_REF], Korjenic 2011, Benfratello 2013], cotton stalk fibers [START_REF] Zhou | [END_REF]], jute fibers [Korjenic 2011], coconut fibers [Panyakaew 2011, Alavez-Ramirez 2012], sunflower pith [START_REF] Vandenbossche | New agro-materials with thermal insulation properties[END_REF], Sabathier 2017], sunflower cake from whole plant [Evon 2014[Evon , 2015]], date palm fibers [Chikhi 2013], etc. The thermal conductivity of insulation boards is often influenced by their density [Khedari 2003[START_REF] Zhou | [END_REF], Panyakaew 2011[START_REF] Vandenbossche | New agro-materials with thermal insulation properties[END_REF], Benfratello 2013, Chikhi 2013, Evon 2014, 2015], and low-density materials are the best insulating materials, with the lowest thermal conductivities. As an example, the thermal conductivity of an insulation board from sunflower pith having a 36 kg/m 3 density is only 38 mW/(m.K) at 25 °C [START_REF] Vandenbossche | New agro-materials with thermal insulation properties[END_REF]]. It is comparable to that of conventional insulation materials like expanded polystyrene (37 mW/(m.K) with a board density of 50 kg/m 3 ), rock wool (36 mW/(m.K) with a board density of 115 kg/m 3 ), and glass wool (35 mW/(m.K) with a board density of 26 kg/m 3 ). Thermal conductivity is higher with medium-density materials: 46-68 mW/(m.K) for coconut husk insulation boards with board densities of 250-350 kg/m 3 [Panyakaew 2011], 74-83 mW/(m.K) for insulation boards from sunflower cake with board densities of 338-375 kg/m 3 [Evon 2015], 82 mW/(m.K) for a cotton stalk fibers insulation board with a board density of 450 kg/m 3 [START_REF] Zhou | [END_REF]], 90-108 mW/(m.K) for hemp fibers insulation boards with board densities of 369-475 kg/m 3 [Benfratello 2013], 104 mW/(m.K) for a coconut coir insulation board with a board density of 540 kg/m 3 [Khedari], and 150 mW/(m.K) for a date palm fibers insulation board with a board density of 754 kg/m 3 [Chikhi 2013]. Nevertheless, such boards are viable options for use in building insulation (walls and ceilings). A strategy to significantly improve the heat properties of insulation fiberboards is the use of natural binders, first solubilized in water and then mixed to the crop residue before compression molding plus drying [Mati-Baouche 2014, Evon 2015]. These external binders are used with physical curing, adhesion being achieved when water has evaporated. It then results in the formation of mostly hydrogen bonds between the biopolymer in the glue and the lignocellulosic compounds (mainly cellulose, and hemicelluloses) in the materials to be joined. When used, natural binders favor both good cohesion and low density for the composite material, thus improving its thermal insulation ability. As an example, it is possible to manufacture insulating bio-based composites according to this technique from sunflower stalks particles and chitosan [Mati-Baouche 2014]. Composites with a thermal conductivity of only 56 mW/(m.K) and a maximum stress of 2 MPa are obtained with a ratio of chitosan of 4.3% (w/w) and a size grading of particles above 3 mm. In the same way, insulation fiberboards made from cake generated during the biorefinery of sunflower whole plant in a twin-screw extruder and three different natural binders (i.e. starch, gelatin, and casein) revealed interesting heat insulation properties [Evon 2015]. There, the medium-density board containing 20% starch-based binder was a good compromise between mechanical and heat insulation properties (347 kPa flexural strength and 78 mW/(m.K) thermal conductivity). These thermal and mechanical performances are competitive with those of other insulating bio-based materials available on the market. When placed in walls and ceilings, such boards could thus contribute to the thermal insulation of buildings. This study aimed to manufacture new thermal insulation fiberboards with improved thermal and mechanical properties, by compression molding at ambient temperature plus drying, from a mixture of a starchbased binder in water solution and coriander straw, and further to evaluate the influence of operating conditions (i.e. straw mechanical pretreatment, binder content, and straw mass) on their density and their flexural and heat insulation properties. The opportunity to use raw materials (i.e. milled or extrusion-refined straw) as loose fill in the attics of houses has been also investigated.

MATERIALS AND METHODS

Raw materials

Coriander straw of French origin (GSN maintenaire variety) was supplied by GSN Semences (France). Prior to the extrusion refining process, the coriander straw was crushed using an Electra Goulu N (France) hammer mill fitted with a 12 mm screen, and then an Electra F3 hammer mill fitted with a 7.5 mm screen. It showed a moisture content of 8.9±0.1%. The natural binder used in this study has been chosen because of its commercial availability. It was a starchbased binder supplied by Bostik (France) with reference number 28474, and commonly used as a glue for wallpapers. Its starch content was 85%.

Analytical methods

The moisture content of coriander straw was determined according to French standard NF V 03-903. The mineral content was determined according to French standard NF V 03-322. The oil content was determined according to French standard NF V 03-908. The protein content was determined according to French standard NF V 18-100. The three parietal constituents (cellulose, hemicelluloses, and lignins) were estimated using the ADF-NDF method of [START_REF] Van Soest | Use of detergents in the analysis of fibrious feeds. IV. Determination of plant cell wall constituents[END_REF], 1968]. Similarly, the water-soluble components were estimated by measuring the mass loss of the test sample after 1 h in boiling water. All determinations were carried out in duplicate.

Twin-screw extrusion refining

A Clextral Evolum HT 53 (France) co-rotating and copenetrating twin-screw extruder was used for the fiber refining of the coriander straw material. The extruder barrel, with a length of 1.9 m, consisted of eight modules, each 4D in length (with D corresponding to the screw diameter, i.e. 53 mm), except for module 1, which had an 8D length. Barrel modules 2 to 8 were temperature controlled. Feed material (i.e. milled coriander straw) was introduced near the first module with a Coperion K-Tron SWB-300-N (Germany) gravimetric feeder. No outlet restriction was applied and the material exited the extruder barrel at atmospheric pressure. Water was injected with two different flow rates (15 kg/h and 6 kg/h, respectively) at the end of module 3 using a Clextral DKM Super MD-PP-63 (France) piston pump, while the straw feed rate was kept constant at 15 kg/h. Two different extrusion-refined materials were thus produced, A and B, corresponding to L/S ratios of 1.0 and 0.4, respectively. An aqueous solution of borax (47.3 g/L) was also used for the 1.0 L/S ratio only, thus leading to the production of a third extrusion-refined material, A*. The screw configuration that was applied was the same as in a previous study [Uitterhaegen 2017]. The extruder screw speed was 150 rpm and the extrusion temperature was set at 110 °C.

Particle size distribution

The morphological characteristics of the milled coriander straw fibers were determined through optical microscopy with a Nachet Z 45 P, ×15 (France) binocular magnifier. Five different images were taken using the Archimed 4.0 (France) software and the measurements were made with the ImageJ (USA) software. A TechPap MorFi (France) Compact analyzer equipped with a CCD video camera was used for the morphological analysis of extrusion-refined materials. About 30,000 fibers were analyzed with MorFi v9.2 software. This software calculated a number of parameters, including mean fiber length, mean diameter and the fines percentage (fibers shorter than sample of 2.5 g of dry material was diluted in 1000 mL distilled water, with mechanical stirring to break up aggregates. A 100 mL aliquot of the mixture was taken and diluted (1:10) by adding distilled water to make the volume up to 1000 mL. This process was repeated twice until a consistent concentration of 25 mg/L was achieved. In addition, it was evidenced that dilution in distilled water did not induce fiber swelling, thus not disturbing the measurement. All characterizations were performed in duplicate.

Apparent and tapped densities

For the tapped density of bulk materials (i.e. milled and extrusion-refined straw), the measurement used a Granuloshop Densitap ETD-20 (France) volumenometer, and the corresponding apparent density, i.e. before compaction, was determined at the same time. Determinations were carried out in duplicate.

Manufacturing of insulation blocks

Dependent on the tapped density of extrusion-refined materials, the minimal mass of straw to be used for compression molding was determined experimentally to reach sufficient cohesion for the insulation block, thus allowing its handling before drying and its cutting after drying without risk to break it. It was 150 g for the A extrusion-refined straw and 210 g for the B one. Two other masses were also tested, representing 133% and 166% of the minimal mass, respectively. The starch-based binder was first solubilized in distilled water at room temperature for 10 min under stirring. Binder solutions were then mixed manually for 5 min with the extrusion-refined straw, corresponding to binder contents (w/w) of 10, 15, and 20%, respectively. The mixtures were molded at room temperature by compression inside an aluminum mold during 30 sec using a hydraulic press, producing 150 mm × 150 mm fiberboards with 5 cm thickness. Two insulation blocks were manufactured for the eighteen molding conditions tested, including extrusion-refined material, binder content and raw material mass (Tab. 1). They were dried at 60 °C using a France Etuves XL2520 (France) ventilated oven to eliminate the water added for binder dissolution, and they were then equilibrated in a climatic chamber (60% RH, 25 °C) for three weeks before any analyses. A first fiberboard was used to assess the mechanical properties for bending, and a second one for measuring the heat insulation properties. An insulation block from milled straw was also produced using the following conditions: a mixture made of 250 g milled straw, 44.1 g starch-based binder (i.e. 15% in proportion to the sum of the masses of straw and binder), and 500 g water, 87 kPa applied pressure, 30 sec molding time, and 80 °C drying temperature.

Mechanical properties for bending

Measurement of the flexural properties of the test specimens according to French standard NF EN 310 was undertaken using an Instron 33R4204 (USA) universal testing machine fitted with a 500 N load cell. Properties covered the flexural f) and the elastic modulus (Ef). Insulation blocks were equilibrated for three weeks in a climatic chamber (60% RH, 25 °C), and then 30 mm wide test specimens were cut and their thickness measured at three points and their length at two points, with a 0.01 mm resolution electronic digital sliding caliper. Thickness (t) and length (l) mean values were recorded to calculate the specimen volume, and they were all weighed to calculate mean apparent density (d). The test speed was 2 mm/min with 80 mm grip separation. All determinations were carried out four times.

Heat insulation properties

A Neotim FP2C (France) thermal conductivimeter with a hot wire was used to measure the thermal conductivity of bulk materials and insulation blocks at 25 °C. According to the technical specifications provided by the manufacturer, the accuracy on thermal conductivity is of 5%. The hot wire method is a transient technique in which an increase of the material temperature is measured. A linear wire, assumed to be of infinite length and negligible diameter, generates heat under the influence of an electric current. Here, the wire and the associated thermocouple were included in a Kapton probe placed between two samples of the material. Then, solving the heat conduction equation in cylindrical coordinate, assuming a semi-infinite material, the thermal conductivity ( ) can be determined. The associated thermal resistance (R) was calculated for a 5 cm thickness of bulk material or insulation block. All samples were equilibrated in a climatic chamber (60% RH, 25 °C) for three weeks before being tested. All determinations were carried out three times.

RESULTS AND DISCUSSION

Coriander straw characterization

In this study, coriander straw was used as the main raw material, which is the lignocellulosic material consisting of the vegetative stalk parts of the coriander plant. It thus represents the crop residue after the coriander fruits have been harvested. The chemical composition of coriander straw used in this study revealed that it was rich in cellulose (52.5±0.1% of its dry matter) and hemicelluloses (21.2±0.5%), and contained a relatively low amount of lignins (9.8±0.2%). Most nonwoody, lignocellulosic materials show cellulose and lignin contents of 30-45% and 10-25%, respectively [Uitterhaegen 2017]. With a similar composition to jute fiber and wheat straw, and owing to its high cellulose content, which provides reinforcement for fiberboards [Uitterhaegen 2017] and natural capacity for thermal insulation [START_REF] Saiah | [END_REF]], coriander straw shows potential as a raw material to produce cohesive insulation blocks. The complementary chemicals inside coriander straw were hot-water extractives (10.4±0.1%), minerals (4.2±0.2%), proteins (3.7±0.1%), and lipids (0.8±0.1%).

Tab. 1: Molding conditions used for the manufacture of the eighteen insulation blocks.

Tab. 2: Particle size distribution of extrusion-refined materials and milled coriander straw.

Tab. 3: Apparent and tapped densities, and heat insulation properties of bulk materials.

Mechanical pretreatments of coriander straw

Before producing insulation blocks, two different mechanical pretreatments were investigated for coriander straw, i.e. crushing using a hammer mill fitted with a 7.5 mm sieve on the one hand, and extrusionrefining using water (1.0 and 0.4 L/S ratios) or an aqueous solution of borax (1.0 L/S ratio) on the other hand. This resulted in important differences in terms of morphological characteristics (Tab. 2) and fiber densities (Tab. 3). When comparing the grinding process using a hammer mill with twin-screw extrusion, it is clear that the extrusion pre-treatment resulted in a substantial improvement of the coriander fiber morphology, illustrated by a strong increase in the fiber aspect ratio, from 4.5 to 17.6-26.5. Thus, the twin-screw extrusion pre-treatment contributed to improved fiber fibrillation and destructurization, resulting in a more expanded, fluffy appearance of the extrusion-refined straw but also in the generation of fines (41-62%).

Logically, a reduction in the density characteristics of the fiber material was observed with the extrusion treatment: 45-82 kg/m 3 apparent density instead of 95 kg/m 3 for milled straw, and 61-110 kg/m 3 tapped density instead of 115 kg/m 3 . When considering the varying L/S ratio applied during the extrusion process, representing different treatment severities, it can be seen from Tab. 2 and Tab. 3 that a reduction of the L/S ratio from 1.0 (A extrusion-refined material) to 0.4 (B extrusion-refined material) resulted in a slightly decreased fiber aspect ratio (from 26.5 to 22.9), and increased apparent density (from 45 to 82 kg/m 3 ). This was mainly attributed to the reduction in fiber length (from 547 to 48 severity of the treatment with a lower L/S ratio.

The same tendency was also evidenced at 1.0 L/S ratio when using an aqueous solution of borax (A* extrusionrefined material) instead of water (A extrusion-refined material): 17.6 aspect ratio and 59 kg/m 3 apparent density. However, for future heat insulation applications, the use of borax during the twin-screw extrusion refining treatment allowed fire-proofing of straw.

Heat insulation properties of bulk materials

Looking at the heat insulation properties of bulk materials (47.3-52.5 mW/(m.K) thermal conductivity, corresponding to a thermal resistance varying from 1.06 to 0.96 (m 2 .K)/W for a 5 cm thickness) (Tab. 3), they could be considered as viable solutions for building insulation when used as loose fill in the attics of houses. And, because the A extrusion-refined straw was the least dense bulk material, it logically revealed the most promising heat insulation properties (47.3 mW/(m.K) thermal conductivity). The use of an aqueous solution of borax (A*) instead of only water (A) resulted in a very slight increase in thermal conductivity (49.3 mW/(m.K)), giving the opportunity of applying fire treatment in anticipation of future commercial applications.

Bending and heat insulation properties of insulation blocks

Eighteen insulation blocks were manufactured through compression molding using different molding conditions (Tab. 1). Conditions included the extrusion-refined material (A or B), the starch-based binder content (10-20%) and the raw material mass (minimal, +33% or +66%). Compression molding was conducted at room temperature, generating blocks with consistently 5 cm thickness.

No problem was observed for the binder dissolution in distilled water. In the same way, manual mixing of the binder solution with extrusion-refined materials was effective for all molding conditions tested. The eighteen insulation blocks were carefully removed from the aluminum mold immediately after compression molding. They were then dried at 60 °C using a ventilated oven to eliminate the water added for binder dissolution.

Depending on the molding condition used, the drying duration varied from 38 to 72 h. All insulation blocks produced were cohesive mixtures of the extrusion-refined straw and the starch-based binder, the latter ensuring material cohesion and the lignocellulosic fibers acting as reinforcement.

Conditioning in the climatic chamber was conducted immediately after production in order to assess the mechanical and heat insulation properties of insulation blocks from equilibrated materials. The moisture content of equilibrated blocks was 8-10%.

The density of the equilibrated insulation blocks was greatly affected by the molding conditions, varying from 127 to 292 kg/m 3 . The B extrusion-refined straw consisting of smaller solid particles generated denser boards. In the same way, an increase in the board density was also observed when using more binder or more total raw material.

The bending properties of insulation blocks were also clearly influenced by the molding conditions, and could be correlated to their densities (Fig. 1 for flexural strength at break and Fig. 2 for elastic modulus). Indeed, the higher the insulation block density, the higher its mechanical properties. The effect of formulations on the bending properties can be also clearly underlined. For example, the AX formulations (extrusion-refined material with longer fibers plus low binder content) revealed the highest values regarding the flexural strength at break and the elastic modulus when the insulation block density was under 200 kg/m 3 . On the contrary, the BZ formulations (extrusion-refined material with shorter fibers plus high binder content) gave the lowest curve of bending properties versus density.

Fig. 1: Flexural strength at break of the eighteen insulation blocks, as a function of their density. Similarly, the thermal conductivity of insulation blocks depended on the molding conditions, its increase being observed progressively with increasing density (Fig. 3).

In parallel, the thermal resistance was reduced (Tab. 4).

In conclusion, the insulation blocks that revealed interesting properties for both thermal insulation and mechanical behavior were obtained from the A extrusion-refined material (consisting of longer fibers) and an intermediate starch-based binder content (15%). The AY2 insulation block (163 kg/m 3 density) presented a good compromise between insulation performance (62.3 mW/(m.K) thermal conductivity, corresponding to a 0.80 (m 2 .K)/W thermal resistance for a 5 cm thickness) and mechanical properties (11.4 kPa flexural strength at break and 34.4 kPa elastic modulus), the latter being sufficient for both handling and cutting.

Fig. 3: Thermal conductivity at 25 °C of the eighteen insulation blocks, as a function of their density.

An insulation block was also successfully produced through compression molding using milled straw (Fig. 4). Molded under 87 kPa applied pressure and from a mixture made of 250 g milled straw and 44.1 g starchbased binder (i.e. 15% in proportion to the sum of the masses of straw and binder, or 17.6% in proportion to the only straw mass), this nineteenth insulation block was thinner (3.5 cm thickness instead of 5 cm for blocks made from extrusion-refined straws). It revealed a 155 kg/m 3 density and a 55.6 mW/(m.K) thermal conductivity, corresponding to a 0.90 (m 2 .K)/W thermal resistance for a 5 cm thickness. Thus, it was slightly better thermal insulator in comparison to the AY2 block made from the A extrusion-refined material. In addition, its bending properties were far superior, i.e. 64.5±2.9 kPa for flexural strength at break and 2.1±0.4 MPa for elastic modulus. Because fiber bundles inside milled straw were much bigger than inside extrusion-refined materials, this contributed to a reduced surface area per unit weight of crushed material. Thus, the binder added in that case at an amount of 17.6% (in proportion to straw mass) surely allowed complete wetting of the fibers, resulting in an improved cohesion. As for the AY2 material, this optimal block from milled straw could also be used for the thermal insulation of buildings, including the filling of walls, interior partitions, etc. For future work, the resistance of these building insulation materials to fire, rodents, pests, and fungi should be investigated to ensure the preservation of their properties of use over time. An improvement in their water resistance will also need to be considered.

Tab. 4: Density, heat insulation and bending properties of insulation blocks.

CONCLUSION

New thermal insulation blocks were manufactured from coriander straw. All materials were cohesive mixtures of a starch-based binder and fibers from straw. The binder contributed to the board cohesion, and entanglement of the fibers acted as reinforcement. The operating conditions (i.e. straw mechanical pretreatment, binder content, and straw mass) had an important influence on board density, and on mechanical and heat insulation properties, the density varying from 127 to 292 kg/m 3 . The heat insulation properties improved with decreasing density. From the A extrusion-refined straw, a board containing 15% starch-based binder (163 kg/m 3 density) was a good compromise between mechanical and heat insulation properties (62 mW/(m.K) thermal conductivity). The latter were even better when starting from milled straw (7.5 mm sieve): 155 kg/m 3 density, and only 56 mW/(m.K) thermal conductivity. Placed in walls and ceilings, such insulation blocks could be used as thermal insulation in the construction industry. The heat insulation capacity of the bulk materials was even better, especially for the A extrusion-refined straw (47 mW/(m.K) thermal conductivity), thus allowing their use as loose fill in attic spaces for the thermal insulation of houses.

Fig. 2 :

 2 Fig. 2: Elastic modulus of the eighteen insulation blocks, as a function of their density.

Fig. 4 :

 4 Fig. 4: Photograph of raw straw, milled straw and insulation block from the latter (from left to right).

ACKNOWLEDGMENTS

The authors would like to express their sincere gratitude to Ovalie Innovation (Auch, France) for providing financial support and for supplying the coriander straw material used for the purpose of this study. The authors would also like to thank Marc Delgado-Paperera i Materials Polímers (LEPAMAP) research group, University of Girona, Spain) for conducting the fiber morphology determinations from extrusion-refined materials.