Dr Zhixiong Li
email: zhixiong.li@ieee.org

Dr Shuaishuai
email: shuaishuai.sun.b1@tohoku.ac.jp

Dr R Malekian
email: reza.malekian@ieee.org

Prof Zhenjun Ma
email: zhenjun@uow.edu.au

Weihua Li
email: weihuali@uow.edu.au

Keywords: laser scanning, GPS-denied environments, positioning accuracy, robust weight total least squares, full information maximum likelihood optimal estimation

The positioning accuracy of the mobile laser scanning (MLS) system can reach the level of centimeter under the conditions where GPS works normally. However, in GPS-denied environments this accuracy can be reduced to the decimeter or even the meter level because the observation mode errors and the boresight alignment errors of MLS cannot be calibrated or corrected by the GPS signal. To bridge this research gap, this paper proposes a novel technique that appropriately incorporates the robust weight total least squares (RWTLS) and the full information maximum likelihood optimal estimation (FIMLOE) to improve the positioning accuracy of the MLS system under GPS-denied environment. First of all, the coordinate transformation relationship and the observation parameters vector of MLS system are established. Secondly, the RWTLS algorithm is used to correct the 3D point observation model; then the uncertainty propagation parameter vector and the boresight alignment errors between the laser scanner frame and the IMU frame are calibrated by FIMLOE. Lastly, experimental investigation in indoor scenarios was performed to evaluate the effectiveness of the proposed method. The experimental results demonstrate that the proposed method is able to significantly improve the positioning accuracy of an MLS system in GPS-denied environments.

I. INTRODUCTION

he mobile laser scanning (MLS) system is a kinematic platform and mainly consists of laser scanner, inertial measurement unit (IMU), GPS receiver as well as other ancillary devices on a moving platform; this system can be used to generate 3D point cloud data of the surrounding scene with high precision, convenience, efficiency and effectiveness. These data are useful for many applications such as 3D landscape modeling for visualization in planning, simulations for environmental management and navigation for robots and vehicles. For these practical applications, positioning accuracy is essentially important, particularly in GPS-denied environments where the position accuracy can be reduced to the decimeter or even the meter level due to the trajectory errors of the laser scanner and the IMU drift. Hence, it is crucial to improve the positioning accuracy of MLS in GPS-denied environments [START_REF] Li | A density-based clustering method for urban scene mobile laser scanning data segmentation[END_REF][START_REF] Zhou | Railway tunnel clearance inspection method based on 3D point cloud from mobile laser scanning[END_REF][START_REF] Nurunnabi | Locally weighted regression techniques for ground surface points filtering in mobile laser scanning three dimensional point cloud data[END_REF].

In GPS-denied environments, the main errors affecting the positioning accuracy in an MLS system can be categorized into three main sources [START_REF] Xu | Error analysis and accuracy assessment of mobile laser scanning system[END_REF][START_REF] Hauser | Calibration and accuracy analysis of a low-cost mapping-grade mobile laser scanning system[END_REF][START_REF] Yi | Performance analysis of mobile laser scanning systems in target representation[END_REF][START_REF] Morales | Boresight calibration of construction misalignments for 3D scanners built with a 2D laser rangefinder rotating on its optical center[END_REF][START_REF] Mezian | Uncertainty Propagation for Terrestrial Mobile Laser Scanner[END_REF]. The first one is laser scanner error. The errors in range and angular measurements of the time-of-flight laser scanner lead to the uncertainty in locating the actual positions of the scanned points. IMU attitude error is the second inaccuracy source. The principal role of IMU is to provide angular velocity observations which can be integrated into angular orientation information (roll, pitch, and heading) of the IMU body frame with respect to the MLS system local frame. Together with position data, it enables the point data in the MLS local frame to be transformed into the earth-centered-earth-fixed frame. Thus, all points in the point cloud are projected into a common reference frame and the IMU attitude data may affect every point. Therefore any uncompensated errors from the IMU will directly impact the geometric quality of the point cloud. The third source is boresight alignment error. The so-called boresight angles are the orientations of the laser scanner frame with respect to the IMU body frame and the lever arm offset. Because the boresight angles cannot be measured directly, they are obtained through a calibration process. There are inevitable residual adjustment errors present in the alignment estimates. The lever arm offset values can be obtained either through calibration or Improving positioning accuracy of the mobile laser scanning in GPS-denied environments: An experimental case study WI. Liu, Zhixiong Li, Shuaishuai Sun, Reza Malekian, Zhenjun Ma, Weihua Li T measurement. Noting that the required boresight angles are vector components, not just lengths, some realization of the relevant coordinate systems is necessary.

In order to improve the position accuracy and eliminate error sources for the MLS system in GPS-denied environments, previous researchers have made great progress on data-driven and model-driven techniques. The data-driven techniques can be directly used to correct the laser scanning point clouds data based the on ground control points and the different correction algorithms [START_REF] David | Geometric validation of a ground-based mobile laser scanning system[END_REF][START_REF] Rau | Direct sensor orientation of a land-based mobile mapping system[END_REF]. Mao et al. [START_REF] Mao | A least squares collocation method for accuracy improvement of mobile LiDAR systems[END_REF] proposed a least squares collocation (LSC) technique to increase the mobile LiDAR accuracy in GPS hostile environments. Shi [START_REF] Shi | Adaptive mapped least squares SVM-based smooth fitting method for DSM generation of LIDAR data[END_REF] presented an adaptive mapped smooth fitting method on the basis of least squares support vector machine (LS-SVM) for digital surface model generation of airborne LiDAR scanning data. Gneeniss et al. [START_REF] Gneeniss | In-flight photogrammetric camera calibration and validation via complementary LiDAR[END_REF] utilized a total least squares (TLS) surface matching algorithm to align a dense network of photogrammetric points to the LiDAR reference surface, allowing for the automatic extraction of LiDAR control points. Hans-Gerd [START_REF] Maas | Least-squares matching with airborne laser scanning data in a TIN structure[END_REF] presented a formulation of least squares (LS) matching based on the original data points in a triangulated irregular network structure, avoiding the degrading effects caused by the interpolation. Lee et al. [START_REF] Lee | Effective reduction of horizontal error in laser scanning information by strip-wise least squares adjustments[END_REF] solved the observation equations of the airborne laser scanning system using the LS method. A set of affine transformation equations were produced to adjust the horizontal error. Xi et al. [START_REF] Xi | Total least-squares methods for active view registration of three-dimensional line laser scanning data[END_REF] proposed the TLS method for active view registration of three-dimensional line laser scanning data. Furthermore, the simultaneous localization and mapping (SLAM) algorithms have attracted extensive attention in recent years. SLAM aims to build a map of the environment while simultaneously determine the position of a moving sensor platform (most notably in photogrammetry and computer vision) [START_REF] Wang | A robust registration method for autonomous driving pose estimation in urban dynamic environment using LiDAR[END_REF][START_REF] Liu | Error modeling and optimal estimating of laser scanning aided inertial navigation system in GPS-denied environments[END_REF][START_REF] Li | Multi-scale site matching for vision-only intelligent vehicles selflocalization[END_REF]. SLAM can use the nonlinear least squares (NLS) to correct the position information.

The model-driven techniques usually establish the mathematical models for the MLS system and analyze the error sources to calibrate the errors. Chen et al. [START_REF] Chen | Boresight calibration of airborne LiDAR system without ground control points[END_REF] proposed a new method for boresight misalignment calibration of the charge-coupled device CCD camera in an airborne LiDAR system without ground control points. Darren et al. [START_REF] Darren | Calibration and accuracy analysis of a low-cost mapping-grade mobile laser scanning system[END_REF] used a planar patch least-squares approach to determine the boresight angles and the lever-arm offsets of an MLS, and calibrated the boresight MLS operation in a backpack mode to acquire multiple data sets in an area that contains dense ground control points. Habib et al. [START_REF] Habib | Error budget of LiDAR systems and quality control of the derived data[END_REF] provided a tool for the quality control of the LiDAR point cloud and analyzed possible systematic and random errors as well as their impact on the laser scanning surface. Ye et al. [START_REF] Ye | Dynamic calibration of exterior orientations for vehicle GPS/IMU/LS laser imaging system[END_REF] proposed a calibration method with small rotation-angle corrections for the exterior orientations of a vehicle laser imaging system.

Although LS, LS-SVM, NLS, LSC and TLS can be used to resolve the problem of parameter estimation in laser scanning point clouds data, the estimated parameters and the position accuracy can be influenced when 3D point observation model and uncertainty propagation parameter vector of MLS system are contaminated by laser scanner errors and attitude errors of IMU. The main limitation of the existing methods is that they assume that the trajectory errors and IMU drift errors in the MLS system are very small or negligible. If this assumption is not available or sufficiently accurate, then the estimated parameters and position accuracy will significantly degrade [START_REF] Wang | A robust weighted total least squares algorithm and its geodetic applications[END_REF][START_REF] Yan | 3D-laser-based scene measurement and place recognition for mobile robots in dynamic indoor environments[END_REF]. Literature review shows that the robust weight totals least squares (RWTLS) method is based on the robust estimation equivalent weight rule and the Newton-Gauss method, which utilizes standardized residual to construct the weight factor function and uses the median technique to acquire a variance component estimator. Therefore, RWTLS possesses good robustness in both 3D point observation model and uncertainty propagation parameter vector in MLS [START_REF] Grejner-Brzezinska | A robust solution to high-accuracy geolocation: Quadruple integration of GPS, IMU, pseudolite, and terrestriallaser scanning[END_REF][START_REF] Hirota | IMU sensor based human motion detection and its application to braking control of electric wheeled walker for fall-prevention[END_REF][START_REF] Huang | Accurate 3-D position and orientation method for indoor mobile robot navigation based on photoelectric scanning[END_REF]. However, RWTLS cannot calibrate the boresight alignment errors of MLS. To solve this problem, the full information maximum likelihood optimal estimation (FIMLOE) can be integrated with RWTLS. The FIMLOE method uses an approximate Newton method to identify boresight alignment parameters between the IMU and the laser scanner, which enables FIMLOE to yield very low proportion of convergence failures and to provide near-optimal boresight alignment errors [START_REF] Elseberg | Algorithmic solutions for computing precise maximum likelihood 3D point clouds from mobile laser scanning platforms[END_REF][START_REF] Eberhard | Accuracy of maximum likelihood and least-squares estimates in the lidar slope method with noisy data[END_REF][START_REF] Savalei | Robust two-stage approach outperforms robust full information maximum likelihood with incomplete nonnormal data[END_REF]. Hence, it is reasonable to develop a new RWTLS-FIMLOE method to improve MLS positioning accuracy in GPS-denied environments. However, to our best knowledge, RWTLS-FIMLOE has not been found in literature.

For this reason, this paper proposes a novel RWTLS-FIMLOE method to improve the MLS system positioning accuracy in GPS-denied environments. The contributions of this paper are: (1) the proposed RWTLS-FIMLOE method is introduced to correct the 3D point observation model and the uncertainty propagation parameter vector; it is also used to calibrate the boresight alignment errors between the laser scanner frame and the IMU frame; (2) an MLS mathematical model is established to analyze the effects of each individual error source on the positioning accuracy. Experimental results demonstrate that the proposed method achieved significant improvement in terms of the MLS positioning accuracy in GPS-denied environments.

II. MODELING FOR MLS SYSTEM

In GPS-denied environments, the IMU provides the position and orientation for moving platform during MLS measuring process. The laser scanner measures the distances from the sensor to the scanned target point and records the rotating angles of the laser beam. Thus, the accuracy of the laser scanning point clouds depends on the quality in terms of reliability and accuracy of the moving platform trajectory. However, the IMU provides the MLS system a moving trajectory, instead of the fixed control points through a traditional control network. As a result, both of the IMU and the laser scanner direct referencing coordinate frame make up the total error budget together. Generally, an MLS system includes three basic coordinate frames. Figure 1 shows the relationship among these frames [START_REF] Mezian | Uncertainty Propagation for Terrestrial Mobile Laser Scanner[END_REF][START_REF] Leslar | Error analysis of a mobile terrestrial LiDAR system[END_REF]:

(1) The world coordinate frame   W .

(2) The integrated moving platform and the IMU coordinate frame   I .

(3) The laser scanner coordinate frame   L can be expressed as:

L . P W Z W Y W X I Z I Y I X L Z L Y L X   W   I   L W i P I W i P  L i P L I i P 
W I W i I W i I W P R P T    (1)
( , , )

L I L i L I i L I P R P T       (2)
where LI R  is the constant rotation matrix between frame  

W ENU I NED x y z R R R R R     (3)
L i i i LL i i i i L ii X PY Z                   (4)
where  

0 0 0 T L L L X Y Z
is the mirror center offset of laser scanner,  is the measurement range,  and  are the vertical angle and the horizontal angle of laser scanner, respectively. According to the Equations ( 2)-( 4), the Equation ( 1) can be rewritten as: 5) shows that the coordinates of points W i P in the frame   W depends on the observation parameters vector:

0 0 - 0 - - - - - ( , , ) ( ) ( ) ( ) cos sin cos cos sin L IL L W L i I W W ENU i x y z NED I W W i i i L I i I W i i i L I i i L I X RY XX Z Y R R R R Y X ZZ Y Z                                                            (5) Equation (
0 0 0 , , , , , , , , , , , , , , , , ,
T L L L x y z L I L I L I I W I W I W X Y Z X Y Z X Y Z                     
Then the RWTLS-FIMLOE integration technique is used to compute the uncertainty of  for every 3D point W i P .

III. RWTLS-FIMLOE INTEGRATION METHOD

The block diagram of the proposed RWTLS-FIMLOE algorithm is illustrated in Figure 2. Firstly, the boresight alignment parameter errors between the laser scanner and IMU are calibrated using FIMLOE algorithm. According to Equation ( 5), the laser scanning points are transformed into the frame   W . Then the trajectory errors are corrected, the laser scanning control targets coordinate is extracted, and the uncertainty propagation parameter vector is calculated. Lastly, the 3D point observation model and the uncertainty propagation parameter vector are corrected using RWTLS algorithm.

A. RWTLS Correction for MLS Observation Model and Uncertainty Propagation Vector

According to Equation ( 5), the observation model of W i P can be simplified as [START_REF] Darren | Calibration and accuracy analysis of a low-cost mapping-grade mobile laser scanning system[END_REF][START_REF] Zhou | A mixed weighted least squares and weighted total least squares adjustment method and its geodetic applications[END_REF]: A is unstructured, then the random elements should be calculated firstly and the independent random variables from coefficient matrix are extracted, Equation ( 6) can be rewritten as:

() AP P A E e     (
   () T m a P P I h B a e e       (7)
where B represents the deterministic matrix, h denotes the deterministic constant vector of A , a represents the independent variables vector, m I represents the unit matrix, a e is the random error vector for a . Therefore, the coefficient matrix A is calculated as:

() A ivec h Ba  (8) where the " ivec " denotes the original matrix vector with mn  dimension.

The MLS stochastic model can be expressed as: Supposing that through i th  iteration, the parameter estimator vectors i  can be derived, the predictive residual vector of a is i a e . The right-hand member of Equation ( 7) is expressed through Taylor series expansion at  

2 0 0 ~0, 0 P a P a Q e e Q                 (9)
, i ia e  :          () i TT i m i m a aP T i i i m a P P I h Ba I Be ivec h B a e e A A I Be e                        (10)
where

    () ii i a a A ivec h B a e A ivec
             ( 11
)
where K is the Lagrange multipliers vector with the dimension 1 m .

According to the Equation ( 11), the solution for the function f can be computed as:

        %% % (12)   , , , 1 ˆ0 Pa T a i m a ee a K f e B I Q K e          %% % (13)
, , , ˆ0

Pa T i e e K f AK         %% (14)   , , , ˆ0 Pa P T i ee m i a i K f eA P I Be K               %% % % (15)
where "~" and "^" represent the predicted and the estimated, respectively.

According to the Equations ( 12)-( 15), the following equations are obtained:

    1 11 1 ˆii TT i i c i i c i A Q A A Q P A         (16)       1 11 11 ˆˆii i TT i i i i c T i ic a i m A Q A A Q I Be            % (17)
 

1 1 1 îi P P c i i i e Q P A A          % (18)     1 1 1 îi T a a i m c i i i e Q B I Q P A A            % (19)
where

    i TT c P i m a i m Q Q I BQ B I       .
The Equations ( 16)-( 19) denote the iterative procedure of RWTLS algorithm, during this process, the threshold 0  should be given to terminate iteration when

B. FIMLOE Calibration for MLS Boresight Alignment Errors

In Section A, the boresight alignment parameters between the frame   L and the frame   I are assumed to have been calibrated accurately in advance. However, it is not always the case; the calculating method of these parameters is an important problem in the MLS system. Therefore, the FIMLOE algorithm is used to calibrate the boresight alignment errors between the frame   L and the frame  

I .
According to Equation ( 5), the boresight alignment parameters vector is treated as time-invariant tendency variables denoted by  , which can be collected as: 0 , , , , , , ,

T LI L TT           (20)
Therefore, the FIMLOE algorithm is to solve the following optimization problem [START_REF] Balogh | Full information from measured ADC test data using maximum likelihood estimation[END_REF]:

  , ,
arg min , , ,

LI FIMLOE FIMLOE P P f P U V      (21)
where

, LI FIMLOE FIMLOE P  
are the full information maximum likelihood estimation of the state position vector and the alignment parameters vector, respectively;   f  is the joint probability density function; U is the laser scanner measurement of each scanning point in the time instances t at the frame   L ;

V is IMU measurement results in the time instances t at the frame   I .

In the MLS measuring process, the IMU noise process is dependent variable, the laser scanner noise process is independent, and the laser scanner measuring points depend on the value of U in the time instance t . Therefore, the function

 

f  can be written as:

      0 0 0 , , , = , , M T T jt t j t t f P U V f U P f V P          ( 22
)
where M is the total number of the laser scanning points, T is the total measuring time; jt U is the state in the time instance t and it lies on the total measurement points M ; t V is the IMU measurements in the time instance t . The calibration procedure of FIMLOE algorithm mainly includes the following:

(1) Nominal state position vector and alignment parameters vector Although the measurement models of IMU and the estimation process are nonlinear, the nominal state position vector P and the alignment parameters vector  are linearized, and they are perturbed with the state position error and the alignment parameters error P  ,  :

P P P              (23)
(2) Minimal state position vector and alignment parameters vector representation

The transform from minimal states to nominal states can be represented using function P ) and 

) , : ( ) ( ) ()

P H P G P U V G U V                 )) ) ( 24
)
where G is the driving white noise process on the minimal state; H is the derivative of the alignment parameter function regarding to the minimal states function P ) and 

) . ( 3
                       ) ) ) ) (25)
where 2  is referred to the vector Euclidean length; () F  is the FIMLOE minimal joint probability density function.

Therefore, the misalignment calibration error can be computed by Equation [START_REF] Yan | 3D-laser-based scene measurement and place recognition for mobile robots in dynamic indoor environments[END_REF] when more than two controlling points are measured.

IV. EXPERIMENTS

To verify the effectiveness of the proposed algorithm, a series of experiments in indoor and outdoor environments were conducted, as shown in Figure 3. The indoor experiments were performed on the fifth floor of a building in China University of Mining and Technology, and the outdoor experiments were carried out around the teaching building in the campus. The experiments were conducted by using a mobile laser scanning system manufactured by HiScan [START_REF]Velodyne HDL-32E User's Manual[END_REF] (Figure 4), which consists of a laser scanner, an IMU and several digital cameras. The nominal localization accuracy of HiScan in GPS-denied environments can reach 2cm when aided by laser scanning control points (please note that, in this paper, the digital camera was not used). The performance parameters of MLS are listed in Table I.

The laser scanning control points were laid along the four sides of passageway in the indoor experimental zones, and distributed on the five sides of the teaching building. The normal coordinate values of the 20 laser scanning control points were established by using total station systems with higher accuracy (Leica TS15i, angle accuracy: 2", distance accuracy: 3mm+1.5ppm). The MLS system was repeatedly driven four times both clockwise and counter clockwise around the passageway and teaching building at a constant speed. During the experiments, the IMU provided the direct reference from the MLS data with respect to the laser scanning control points. The commonly known estimated trajectory solution from the IMU system provides the position and orientation of the moving platform to convert the local coordinates of laser scanning point cloud into the world coordinate frame. In the indoor and outdoor scene, the sphere targets are used as the laser scanning control points, which are considered as the volumetric targets with 140 mm in diameter. The sphere target is made of high-strength PVC material which can allow the laser scanner obtaining the points data on spherical surface at farther distance. In indoor and outdoor experiments, the 20 laser scanning control points were selected to construct the 3D control network through observing the horizontal direction, the vertical angle, and the slope distance. The laser scanning control points were uniformly distributed in order to provide the sufficient observable access to the targets, to ensure an identical absolute position reference with the indoor control network. In indoor experimental environment, the four GPS reference stations were fixed as the known nominal points; they reached the 3D positional accuracy of 1cm in the geodetic coordinate frame. This accuracy level could provide the reliable reference for MLS system. The indoor network configuration along the passageway measurement is shown in Figure 3(a) and the outdoor network configuration along the teaching building is shown in Figure 3(b).

A. Validation of LS, TLS and RWTLS-FIMLOE Methods for Control Position Accuracy

In order to validate the effectiveness of our proposed method, the positioning accuracy of the mobile laser scanning in the experimental test in Fig. (3) was examined. The performance of the proposed RWTLS-FIMLOE method was compared with that of existing popular algorithms in literature. As introduced in Section I, some popular algorithms such as LS, TLS, LS-SVM, NLS and LSC have been applied to improving the position accuracy and eliminate error sources for the MLS system in GPS-denied environments; however, the LS-SVM, NLS and LSC are subject to different modeling methods, detection distance and experimental environments. For this reason, in this study the RWTLS-FIMLOE method was only compared with LS and TLS in indoor and outdoor experimental environments. In addition, because RWTLS cannot calibrate the boresight alignment errors of MLS and FIMLOE is only a system parameter identifier, it is unnecessary to evaluate the positioning performance of individual RWTLS or FIMLOE.

In the comparative study, all 20 laser scanning control points were used in the experiments and a number of the overall quantitative indexes were calculated including the minimum, maximum, standard deviation and RMS (root mean square) indexes [START_REF] Lichti | Error models and propagation in directly georeferenced terrestrial laser scanner networks[END_REF][START_REF] Laefer | Crack detection limits in unit based masonry with terrestrial laser scanning[END_REF]. As shown in the Figure 5, in indoor environment, RWTLS-FIMLOE method, TLS and LS method were used to estimate the position accuracy of the 20 laser scanning control points (The normal 3D coordinates of 20 control points had been established using high accuracy total station). The position errors Mean, Stdev and RMS of RWTLS-FIMLOE method in 2D (H) and 3D orientation are 1.36 cm, 0.94 cm, 1.37 cm and 1.85 cm, 0.95 cm, 1.86 cm, respectively. Similarly the TLS method in 2D (H) and 3D orientation are 2.00 cm, 1.03 cm, 2.02 cm and 2.47 cm, 1.12 cm, 2.50 cm, and the LS method in 2D (H) and 3D orientation are 2.36 cm, 1.06 cm, 2.38 cm and 3.00 cm, 1.16 cm, 3.03 cm, respectively. As shown in Figure 6, in outdoor environments the RWTLS-FIMLOE, TLS and LS method were used to estimate the position accuracy of the 20 control points, and their performance was compared with the position accuracy of MLS system with GPS supported. The measuring results are as follows: the position errors Mean, Stdev and RMS of MLS system under GPS supported in 2D (H) and 3D orientation are 1.66 cm, 0.85 cm, 1.67 cm and 1.91 cm, 0.86 cm, 1.92 cm, respectively. Similarly, the RWTLS-FIMLOE method are 1.52 cm, 0.92 cm, 1.53 cm and 1.87 cm, 0.93 cm, 1.88 cm, the TLS method are 2.10 cm, 1.08 cm, 2.12 cm and 2.50 cm, 1.13 cm, 2.53cm, and the LS method are 2.42 cm, 1.24 cm, 2.47 cm and 2.85 cm, 1.28 cm, 2.89 cm, respectively. In summary, according to Figure 5 and6, it is found that the proposed method can improve the MLS system position accuracy compared to the TLS and LS method, and it is suitable for GPS-denied environments.

B. test  

Method to Verify the Effectiveness of RWTLS-FIMLOE

To characterize the difference between the pre-surveyed coordinate and the adjusted MLS coordinate under indoor and outdoor environments, test  was constructed to statistically conclude if a sample standard deviation was satisfied with the specifically required accuracy level under the hypothesis.   is the Chi-square test with ( 1) n  degrees of freedom.

It is supposed that the accuracy evaluation requirement of the indoor localization is 2cm horizontal and 3cm vertical accuracy criterion at the 95% confidence level. If the test in Equation ( 28) is rejected at a specific error level of Type I Error, then a different alternate value 2 a  can be chosen as a substitution of 2 0

 to find out the lower bound that can pass the test in Equation [START_REF] Huang | Accurate 3-D position and orientation method for indoor mobile robot navigation based on photoelectric scanning[END_REF]. This lower bound is called the achieved accuracy with the involved samples.

Similarly, this test statistics can also be applied to the RMS value of a group of samples. Supposing that all the tests in the accuracy assessment in this paper were performed at the 5% significance level of Type I Error. Table II and alternate 2  test of the differences between the pre-surveyed coordinates and the original MLS system coordinates using LS, TLS and RWTLS-FIMLOE methods. The achieved accuracies were ±2cm (horizontal) and ±3cm (vertical) at the 95% confidence level according to the alternate 2  test. Using RWTLS-FIMLOE method, the horizontal and the vertical localization accuracy have achieved ±1.82cm and ±2.33cm, respectively, the horizontal and the vertical localization accuracy of TLS and LS method are± 2.03cm, ±2.84cm and ± 2.52cm, ±4.13cm, respectively. Compared with TLS and LS method, RWTLS-FIMLOE method can improve the overall accuracy by 17.96%, 10.35% and 43.58%, 27.28% in horizontal and vertical localization.

The comparisons between the three methods indicated that the RWTLS-FIMLOE method using control points from all side is able to achieve better accuracy in both horizontal and vertical directions compared to LS and TLS methods.

C. The impact of the Number of Laser Scanning Control Points on RWTLS-FIMLOE Method

The main purpose of this test is to investigate the effect of the number of the laser scanning control points on RWTLS-FIMLOE method. There were a total of 20 laser scanning control points in the experimental scene, the position accuracy improving of MLS system solutions based on the RWTLS-FIMLOE methods were compared through the laser scanning control points using different number of control points (20, 12 and 8) incorporating with the same feature constraints. Moreover, the feature constraints were sequentially tested using test

 

to detect any inconsistency with the precious observation group, and only the qualified constrains were used in the RWTLS-FIMLOE method. The plot of the selected control points in the experimental scene is presented in Figure 7. The experimental results are shown in Table III, the correction effectiveness of the mean errors had achieved the best accuracy which is 0.89 cm in 2D(H) and 0.91 cm in 3D by using 20 control points. Moreover, the 2D(H) and 3D orientation accuracies could achieve 0.98 cm and 1.19 cm when only 10 control points were applied. Although using more control points could achieve better accuracy, the positioning performance not be decreased significantly by reducing the number of control points down to 60 percentage of the total number of the control points. Furthermore, for the test where only 8 control points were used, the accuracy became 1.94cm in 2D(H) and 2.54cm in 3D, which were decreased relatively significant in comparison with the above two cases. Based on the results among all test cases, using more control points in the solution refining process could generally achieve better accuracy in both 2D(H) and in 3D. However, the overall accuracies were not decreased significantly if reducing the number of the used control points up to 60 percent of the total number. However, the 3D positioning accuracy was decreased dramatically using only 40 percent of total number in the 3D position. Therefore, it is necessary to select the control points efficiently and effectively by considering the project budget and the minimal required accuracy.

VI. CONCLUSIONS

This paper proposed a novel algorithm which integrates RWTLS and FIMLOE methods to improving the positioning accuracy of the MLS system in GPS-denied environment. This new method inherits the advantages of RWTLS and FIMLOE methods. The primary contributions of this paper are:

(1) An integration method which combines the advantages of RWTLS and FIMLOE is proposed. This new method is able to correct the 3D point observation model and the uncertainty propagation parameters vector, and it is also capable of calibrating the boresight alignment errors between the laser scanner frame and the IMU frame.

(2) This paper established a mathematical model for MLS and analyzed in depth the effects of the individual error source on the error budget of MLS.

(3) The experimental results show that the proposed method can improve the positioning accuracy of the MLS system in terms of Mean, RMS and Stdev in the 2D(H) and 3D orientations compared with TLS and LS methods, , and it is suitable for GPS-denied environments. Furthermore, according to the alternate 2  test, the RWTLS-FIMLOE method can bring improvements in the overall accuracy up to 17.96%, 10.35% and 43.58%, 27.28% in horizontal and vertical localization when compared with TLS and LS methods.

In conclusion, the developed RWTLS-FIMLOE method realized an effective improvement for MLS system position accuracy and exhibited similar performance of TLS. It can be widely applied in MLS surveying engineering, such as 3D indoor modeling for visualization in planning, simulations for environmental management, navigation for robot or vehicle, and surveying for underground mine, and so on. It is able to keep the high accuracy of the laser scanning points cloud data with little uncertainty in GPS-denied environments.

Fig. 1 .

 1 Fig. 1. Coordinate relationship between   W ,   I and   L frame

R

   are the translation matrix and rotation matrix between frame   I and frame   W . ( ) ( ) ( )

  rotation matrix between the North-East-Down coordinates and the East-North-Up coordinate frame.The measuring model of L i P is given by:

Fig. 2

 2 Fig. 2 Flowchart of RWTLS -FIMLOE algorithm.

Q

  can be calculated, which will bring convenience in constructing RWTLS model.

Fig. 3 .Fig. 4 .

 34 Fig. 3. Scatter plot of the 20 laser scanning control points and 4 GPS reference points: (a) indoor environment and (b) outdoor environment.

Fig. 5 .

 5 The position errors of laser scanning control points using LS, TLS and RWTLS-FIMLOE method in indoor environments. (a) Errors in 2D(H) orientation; (b) Errors in 3D orientation.

Fig. 6 .

 6 Fig. 6. The position errors of laser scanning control points using LS, TLS and RWTLS-FIMLOE method in outdoor environments. (a) Errors in 2D(H) orientation; (b) Errors in 3D orientation.

  determine if the errors were significantly biased and if the errors were much larger than what the RWTLS-FIMLOE method has required. Specifically, a test   was built up to test if a mean error value m was significantly different from zero under the null hypothesis: 0 :0 Hm  vs. the alternate hypothesis the mean value of a group error samples; ˆˆ/ m n   is the standard deviation of mean value m ,  is the standard deviation of a group error samples; n is the number of the samples;

  is a given standard deviation that indicates a required accuracy level, and 2 1 n

Fig. 7

 7 Fig. 7 The scatter plot of coordinates in 2D horizontal of experimental scene. (a) 20 used control points, (b) 12 used control points, (c) 8 used control points.

TABLE II .

 II THE ANALYSIS OF THE DIFFERENCE BETWEEN LS, TLS AND RWTLS-FIMLOE METHODS USING

																			 	test
							Two-tailed  -test	2  Test(α=0.05%)vs the 95%accuracy
		Error Statistics			α=0.05% 0 :0 H  	H	0 :	2  	2 1.5	cm	2	22 a H   1 :
	Method	Error (cm)	Mean (cm)	Stdev (cm)	RMS (cm)	NC			C	0.025			2			2 C	0.05		2	a  (cm)
		N	-1.80	2.03	2.65	20	-3.31	2.16
	LS	E U	1.48 -1.25	1.72 1.67	2.22 2.04	20 20	3.21 -2.81	2.16 2.16	61.73	22.36	22.22	2.52
		2D(H)	2.33	2.66	3.46	20					163.63	22.36	21.90	4.13
		N	-0.62	1.21	1.32	20	-1.92	2.16
	TLS	E U	0.65 0.68	1.38 1.31	1.48 1.43	20 20	1.77 1.93	2.16 2.16	38.07	22.36	21.41	2.03
		2D(H)	0.90	1.83	1.98	20					77.69	22.36	22.30	2.84
		N	-0.46	0.99	1.06	20	-1.73	2.16
	RWTLS	E	-0.48	1.13	1.19	20	-1.58	2.16
	-FIMLOE	U	-0.34	1.19	1.20	20	-1.06	2.16	31.57	22.36	21.93	1.82
		2D(H)	0.66	1.51	1.59	20					52.41	22.36	22.29	2.33

TABLE III .

 III THE EFFECT OF LASER SCANNING CONTROL POINT'S NUMBER ON MLS POSITION ACCURACY BASED ON RWTLS-FIMLOE METHOD

	Results	N	With 20 control points (cm) E U 2D	3D	N	With 12 control points (cm) E U 2D	3D	N	With 8 control points (cm) E U 2D	3D
	Min	-2.03 -1.83 -1.96 1.04 1.42 -1.88 -2.27 -2.87 1.29 1.56 -2.94 -2.71 -2.87 1.52 2.87
	Max	1.33	1.51	1.40	2.53 2.76	1.56	1.25	1.48	2.75 3.74	1.56	1.78	3.33	3.46 4.80
	Mean	-0.57 -0.68 -0.21 0.89 0.91 -0.57 -0.80 -0.67 0.98 1.19 -1.56 -1.15 -1.65 1.94 2.54
	RMS	1.21	1.16	1.20	1.67 2.06	1.43	1.28	1.51	1.92 2.45	2.07	1.79	2.32	2.74 3.58
	Stdev	1.12	0.98	1.24	1.49 1.93	1.38	1.05	1.43	1.73 2.24	1.42	1.44	1.71	2.02 2.65

ACKNOWLEDGMENT

We would like to thanks LetPub (www.letpub.com) for providing linguistic assistance during the preparation of this manuscript.

Manuscript received #######supported by National Key R&D Program of China (2018YFC0604503), National Natural Science Foundation of China (U1610251), Applied Fundamental Research project of Qingdao (2019-9-1-14-jch), Taishan Scholar (tsqn201812025), Australia ARC DECRA (No. DE190100931) and the Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions.

Dr.


domain: spi phys chim

text:
E Fries

C Sanchez

G Hug

Comparative Study of Insertion of Light Elements N, O in Ternary Compounds Ti3AlN and Ti3Al(0)

Keywords: Ti-Al-O, Ti-Al-N, phase, microstructure, light element insertion

The region close to Ti3AlX (X= N, O) composition of the Ti-Al-O and Ti-Al-N phase diagrams has been experimentally addressed. Alloys with the compositions TiJAIN and Ti~AIO have been processed, Phase analysis and microstructural characterization have been carried out. Both alloys are found to be multi-phased. Under-stoechiornetric Ti3A10X and Ti3AlNX are found to be hexagonal and tetragonal (perovskite-like structure), respectively. Additional information concerning the crystallographic site of insertion of nitrogen and oxygen has been obtained by Electron Energy Loss Spectrometry (EELS). In the Ti-Al-N perovskite-like compound, with an average composition of Ti3AlNo,45.the tetragonal structure stems from the long range nitrogen-vacancy ordering.

Introduction

Ti-Al based alloys have received a certain attention due to their low density and high temperature strength for structural applications as well as in composites or in the joining and coating technology. Light elements such as oxygen and nitrogen are known to have a noticeable influence on the mechanical properties of the titanium aluminizes. In addition, these elements can affect the limits of the phase diagrams as well as the processing of the alloys. Both TiAl and Ti3Al alloys are ordered compounds based on fcc and hcp host lattice on which atoms are regularly located. Light elements (C, N and O) find suitable location in the octahedral cavities of the fcc and hcp lattice. It has been observed [1] and demonstrated [START_REF] Hug | Gamma Titanium Aluminizes Proceed. Symp[END_REF] that these elements exhibit a higher volubility in cx2-Ti3Al than in yTiA1. This behavior is due to the chemical environment of the octahedral cavities in which oxygen is located. Indeed, the volubility increases as the amount of titanium atoms forming the cavity increases, a situation which k more favorable in Ti3Al than in TiAl since in the former cavities consisting of 6 titanium atoms are present. During these last years increasing interest has been shown in determining experimental and calculated isothermal sections of the Ti-Al-O [START_REF] Zhang | [END_REF]4] and Ti-Al-N [5][6][7] phase diagrams. At least three definite compounds Ti3AlN (cubic perovskite structure), Ti2AlN (H-phase structure) and Ti3AlzNz (trigonal structure) have been reported in the Ti-Al-N phase diagram. It is not the case in the former diagram where only a "Z-phase" with a composition close to Ti5A1302 [8,[START_REF] Zheng | Quadakkers Scripts[END_REF] and the TiA1205 phase [1O] have been reported. The aim of the present work is to obtain a better knowledge of the ternary phase diagrams and the structure of the phases in presence. Beyond the determination of the stable phases in the vicinity of the Ti3AlX region, the following questions are motivating this work: Does exist a Ti3A10 compound with a cubic perovskite structure like Ti3AlN? Or is Ti3A10 always hexagonal in which case there might be no phase boundary 'between the solid solution Ti3Al+0 and a defined compound. Can we experimentally confirm the location of N and O in the [Ti6] octahedral cavities?

Experimental

Samples of compositions Ti3AlN and Ti3A10 have been synthesized from commercial powders of Ti02 or TiN and pure aluminum using HIP. The different initial products have been mixed under ultrasonic conditions and encapsulated into iron under vacuum. The HIP process was conducted under 2 kbars at 1800"C. The samples were then annealed under vacuum at 1300"C for 100 h. Phase analysis, compositional analysis and microstructural characterization have been carefully carried out by X ray diffraction, electron microprobe analysis and scanning electron microscopy (SEM). The [Ti]/[0] and [Ti]/~] atomic ratios were also measured by EELS [ 1 1].

The crystallographic sites of insertion of nitrogen and oxygen have been addressed by Extended Electron Energy Loss Fine Structure (EXELFS) and Electron Energy Loss Near-Edge Structure (ELNES). These experiments are realized in a transmission electron microscope (TEM) Jeol 4000FX operating at 400 kV and fitted with a Gatan parallel electron energy loss spectrometer (PEELS). The spectra are recorded in diffraction mode.

Theoretical tools and data processing

In order to get information on the crystallographic site of the interstitial O or N atom either in [TiG] or [Ti4A12] octahedral cavity [START_REF] Hug | Gamma Titanium Aluminizes Proceed. Symp[END_REF], the multiple scattering (MS) formalism is used to calculate excitation processes, which occur in EELS spectroscopy. When core electron is ejected as the result of the collision with fast incoming electron it is described as an outgoing spherical wave, which in turn is scattered by the surrounding atomic potentials. In a crystal, the interference between the outgoing and backscattered waves is responsible for the modulations of the inelastic cross-section, which are observed at the EELS edges [START_REF] Leapman | Transmission Electron Energy Loss Spectrometry in Materials Science[END_REF]. Thus relatively faint oscillations over a large energy window give rise to the EXELFS signal which Fourier transform gives access to nearest neighbors distances and coordination numbers. More intense oscillations can be observed within the first 20 to 30 eV after the threshold giving rise to the ELNES signal, however their analysis requires to use more sophisticated formalism since they involve mainly a multiple scattering process. In this case only simulations are practical. Extraction of the EXELFS signal %(k) as well as calculation of backscattering phases and amplitudes and fit in real space r are referenced in literature [12] [13]. The ELNES have been simulated using a full multiple scattering approach with a self consistent potential [14].

Results and discussion

Ti-Al-O compound

1.1 Phases and composition analysis

The microstructure with three different phases was clearly evidenced by SEM backscattered electron image. Large grains of about 10-20 pm and small grains of about 2-3 pm are observed. X-ray imaging with each element of the composition (Fig. 1) and quantitative analysis show clearly two different Ti-Al-O phases with different titanium, aluminum and oxygen atomic ratio and a third phase with smaller grains identified as A120s. The Ti and Al atomic compositions of the Ti-Al-O phases have been confirmed by electron microprobe analysis together with a direct determination of the oxygen using Y~Fe~012 standard (Table 1). The two Ti-Al-O phases were identified by X-ray diffraction as cx-Ti containing Al and O in solution and ct2-Ti3Al containing O in solution. The parameters of the hexagonal cell are compared with JCPDS reference parameters in table 2. An increase of c/a ratio mainly due to an increase of c can be observed in both cases, The thermodynamic equilibrium seems attained since only three phases are obtained: ct-Ti(Al,O), TiqAl(O ~.c)and A1Z03. Our experimental range seems to agree with previous phase diagrams [5,6] although no isothermal section of the Ti-Al-O phase diagram was reported up to 1573 K. Electron diffraction patterns combined with EELS quantification [START_REF] Leapman | Transmission Electron Energy Loss Spectrometry in Materials Science[END_REF] on the same uz-Ti~Al (0) grains show an increase of a and c correlated with a decrease of the [Ti]/[0] atomic ratios (Fig. 2). These ratios could only be used for relative comparisons since the overlap of the O K (532 eV) Ti L23 (456 eV) EELS edges prevents a precise quantification. However, it is consistent with the correlation between the parameters and oxygen content,

EELS Fine structure analysis

Previous experiments [START_REF] Hug | Gamma Titanium Aluminizes Proceed. Symp[END_REF] made at the Ti K edge showed the existence of a first interstitial shell oxygen at 2.01 ~of Ti atoms. However, although it proves that Ti-O bonds exist at 2.01 ~distance, this observation cannot distinguish between [TiG] or [Ti4A12] site. For this reason, we have performed an EXELFS experiment at the Al K edge. The Fig. 3 presents the pseudo-radial distribution function obtained from Fourier transform of the EXEL.FS signal at the Al K edge together with a fit of the two first shells. Results are reported in table 3. The two shells are constituted of the first A1-Ti metal-metal neighbors bonds at 2.85 ~and 2.89 ~. No oxygen at the [TiqAlz] site at a 2,03 ~distance from the Al is observed consistently with oxygen in the [TiG]site, More evidence can be found by examination of the O K edge. However, in this case no EXELFS analysis is possible because of the strong overlap between Ti L23and O K. Instead, we have recorded the ELNES of the O K edge and compared it with simulations in which oxygen is inserted at each possible site. The comparison (Fig. 4) shows that the [Tis] site is more likely.

Ti-Al-N compound

Phases and composition analysis

Three phases were clearly evidenced by X-ray, electron diffraction and SEM : Ti~AINX, TizAINX and TiN1.X.A backscattered electron image shows grains 5-50 ~m size of Ti2AlN embedded in the most extended phase Ti3AlN where some smaller grains about 6-10 ~m size of TiN can also be observed. In this work we mainly focus on the main phase (Ti~AINX), thus the detailed analysis of the TizAIN phase will be reported elsewhere. The Ti3AlNX nitride has a cubic perovskite structure. Extra reflections due to superlattice structure are observed on a selected area electron diffraction (SAED) pattern with [001 ] zone axis. They can be ascribed to the formation of nitrogen-vacancy long-range ordering as was already reported [15]. The unit cell of the long-range ordered perovskite is four times larger than that of the simple perovskite and involves four basic cubic cells arranged in a tetragonal structure. Such lowering of the symmetry from cubic to tetragonal gives rise to a slight relaxation from the cubic cell which has been determined by X-ray diffraction, Step scans were made accross the 200, 220 and 311 reflections (cubic notation) showing a splitting of these reflections consistent with the tetragonal relaxation. The lattice parameters are a = 4.093 ~and c =4,148 ~in the pseudo-cubic cell or more adequately in the superlattice structure a = 8,186 ~and c = 4.148 ~. The different interplanar spacings obtained from the fit treatment are given table 4. The overlap of the Ti La and N Ku X-ray fluorescence peaks do not allow SEM nor microprobe quantitative analysis. However quantification is done by EELS [START_REF] Leapman | Transmission Electron Energy Loss Spectrometry in Materials Science[END_REF] since the NK edge at 401 ,6eV and the Ti L23 edge at 456 eV are clearly resolved with this technique. The resulting composition Ti3AlNo,4~is in good agreement with previous observations [15] and with the SAED of the superlattice structure. The thermodynamic equilibrium conditions seem to be attained in this sample as well: the three ternary phases Ti3AlNo.5, Ti2AlN and TiN have been obtained. Our observation seems to be in good agreement with the tri-phased domain reported in previous theoretical and experimental phase diagrams [5][6][7] at 1573 K. Although not quantified, the TiN is more probably TiN1.x .The TiqAlzNz phase which has been mentioned [7,16] was not observed here, it is not surprising since this compound forms only within a narrow temperature range.

EELS fine structures analysis

The EXELFS signal ~(k) is extracted at the Al K edge as described before. Fig. 5 presents the pseudo-radial distribution function obtained from Fourier transform of this signal together with a fit of the first shell. The results are reported table 5, the first shell is constituted of first A1-Ti metalmetal neighbors bonds at 2.86 ~. Nitrogen is not in the [Ti4A12]site at 2.05 ~from Al, the [TiG] site at 3.56 ~could not be reached for insufficient statistics in the signal. Again, the comparison of the N K ELNES with theoretical simulations (Fig. 6) confirms that the nitrogen atoms locate more likely at [TiG]sites.

Conclusions

The ternary phase diagrams Ti-Al-O and Ti-Al-N have been checked at 1573 K for the compositions Ti3A10 and Ti3AlN respectively. Whereas the already reported TiqAINO.s was observed, the cubic TiqAIO was not found. Instead, we found a phase of composition TiqAl@,G which can be described as ct2-Ti3Al with large amount of oxygen in solid solution. For both samples equilibrium conditions seem to be attained and three phases were obtained in both cases : u -Ti(Al,O), ct2-Ti3Al(0) and A1203 in the first case and TiqAINo.5, Ti2AlN and TiN in the second case The Ti3AlNo,5 phase has a perovskite structure with long,-range nitrogen-vacancy ordering giving rise to a superlattice structure four times larger than the cubic one and slight tetragonal relaxation: a =8.186 ~and c =4.148

~. EXELFS at Al K edge and EL,NES at N K and O K edges are consistent with N and Oat [Tib] sites.

[16] M. W. Barsoum

Figure Captions

Fig.( 1 )

 1 Fig.(1) (a) Ti Kct (b) Al Ku (c) O Kcx SEM X-ray images showing three different phases for this Ti-Al-O preparation. The small grains without any Ti are constituted of A1203. Fig.(2) [Ti]/[0] atomic ratios from EELS analysis versus unit cell parameters a and c of the Ti3Al(0) phase determined by electron diffraction on the same area. Fig.(3) Fourier transform of the EXELFS signal at Al K edge in Ti3Al(0) and fit of the two first shells : a) imaginary part b) modulus. Fig.(4) ELNES signal at O K edge in Ti3Al(0): experimental and simulations with O at [TiG]or [TiqA12]site. Fig.(5) Fourier transform of the EXELFS signal at Al K edge in Ti3AlN and fit of the first shell: a) imaginary part b) modulus. Fig.(6) ELNES signal at N K edge in Ti~AINO,j:experimental and simulations with N at [Tib] or [TiqA12]site.

  Fig.3

Fig. 5

 5 Fig.5

Table 1 :

 1 , J. C. Schuster, J, Am. Ceram, Sot, 81 n03 (1998) 785-86, Atomic composition of the Ti-Al-O phases determined by electron microprobe analysis.

	Tables
	element		Ti (at,%)	Al (at.%)	O (at.%)
	Phase 1		65.72 k 0.03 %*	11.04 t 0.02 %*	23.24f 0.4 '%0*
	Phase 2		64.91 f 0.03 yO*	21.82 t 0.02 "A*	13.27 t 0.4 %*
	*statistical precision of the measurements
	Phase	a(JCPDS)~c(JCPDS)~c/a(JCPDS)	a~CA	cla
	cx-Ti	2.950	4.683	1.587	2.944	4,744	1.611
	u2-TiqAl	5.77	4.63	0.802	5.791	4.693	0.810

Table 2 :

 2 Comparison of ct-Ti(Al,O) and a2-Ti~Al(0) parameters determined by X-ray diffraction with (x-Ti and ccz-Ti3Al reference (JCPDS) parameters.

	Path	R (~)	02 (Az)
	A1-Ti	2.85 t 0.02	0.018 t 0.001
	A1-Ti	2.89 + 0.02	0.018 + 0.001

Table 3 :

 3 Ti3Al (0), results of the fit of the Al K edge with FEFFIT, o is the Debye-Wailer factor

	hkl	002	200	022	220	113	311
	dml(~) fit	2.0698	2.0429	1.4567	1.4471	1.2462	1.2360

Table 4 :

 4 Ti3AlN, interplanar spacings determined by X-ray diffraction after fit treatment.

	Path	IR (~)	102 (Az)
	~A1-Ti	12.86 f 0.02	10.012*0.001	1

Table 5 :

 5 Ti3AlN, results of the fit of the Al K edge with FEFFIT.

Acknowledgments

The authors would like to acknowledge T. Ochin, A. Morel, D. Boivin and Y. Pioche from ONERA Chfitillon, France, for their contributions to X-ray diffraction, SEM and microprobe analysis.


Georges Mykoniatis
email: georges.mykoniatis@enac.fr

Stephane Puechmorel
email: stephane.puechmorel@enac.fr

Isabelle Laplace
email: isabelle.laplace@enac.fr

THE CHALLENGE OF THE PASSENGER CENTRIC TRANSPORTATION -SMART4PAX

Keywords: Passenger Centric Transportation, Data Sharing, Collaborative Decision Making, Multi-modality, Safety security, travel time, SMART4PAX

Today, new services, offers, products open a realm of possibilities to the imagination of individuals, leading to profound changes in the way we move. While transport was very often viewed as a constraint, mobility becomes a way of life. In human societies, mobility is increasingly understood in terms of social links' creation, opportunities and synergies rather than pure problem of travelled distance (Amar [1]). The Human mobility becomes individual in the sense where each one conceives his own "mobility cocktail". Changes in individual preferences, behaviors and lifestyles together with innovative Information Communication Technologies (ICT) are likely to impact the way travelers plan their mobility behavior and value their own mobility choices. This mobility behavior and choices concern all the different transport modes that have to be used to perform the full trip i.e. the door-to-door travel is therefore intrinsically multimodal. Multimodality presupposes the availability of a large range of transport options and various combinations of them that can transfer passengers from their origin to their destination. One stake of multimodality is the ease to change scale, for instance when travelling successively by air with an intercontinental flight and by route with bus or car. Communication technologies and data sharing henceforth are essential part of transport systems. They enable transport mode operators to propose new services to facilitate the traveller's empowerment and autonomy. Beyond traditional transport modes organized by operators, additional services indeed provide travelers with means to organize, control and optimize their own door-to-door mobility and to turn their travel time into useful or even enjoyable time. These services add value to the travelers' mobility space-time and may change their perception of travel time. Along the way these services can impact travelers' mobility choices. First A. Author, Second B. Author, and Third C

Today the transportation system is oriented on the various means of transport, the objective here is to put the passenger in the centre of the transportation system, and to provide Smart solutions to improve Passenger Seamless Mobility and experience.

We will address the challenge of the Passenger Centric Transportation through 4 pillars:

• Smart for Enhancing passenger experience, new relation to the journey putting the passenger in the centre of the decision-making process, taking into account its profiles its needs and expectations.

• Smart for Safety and security, (e.g. secure exchanges to reduce the constraint of controls in the airports and Train stations improve the safety and security holistic survey approach)

• Smart for Multi-modality, efficient combination of various transport mode according to the expectations of the passenger, an "Unique electronic ticket » for the whole journey…, the environmental issues…

• Smart for Mobility as a service, new set of services for the traveller journey, new services to facilitate the traveller's empowerment and autonomy. Beyond traditional transport modes organized by operators, additional services indeed provide travellers with means to organise, control and optimise their own

INTRODUCTION

The transportation domain is changing radically since the last decade, our relation to the travel and to mobility is now passing through new services, offers. mobility becomes a way of life. In the 90 th , the challenge of the air transportation was to improve the collaboration between the various actors for a more safe and efficient transportation. That conduces to the evolution of the Central flow management unit to the Network Manager [START_REF]An Introduction to the EUROCONTROL Network Manager Operations Centre -January 2019 -EUROCONTROL[END_REF].

The Collaborative Decision Making concept putting in place collaborations with airlines, Air National Service Providers, Airports take shape in various airports [START_REF] Eurocontrol | IATA : Airport CDM Implementation Manual[END_REF]. In the last 5 years, the transportation domain change its paradigm and start a mutation putting the passenger in the center of the decision process and introduce the concept of Mobility as a service (Maas) https://en.wikipedia.org/wiki/Mobility_as_a_service. The SMART4PAX concept is based on 4 pillars:

• Smart for Enhancing passenger experience, new relation to the journey putting the passenger in the centre of the decision-making process, taking into account its profiles its needs and expectations.

• Smart for Safety and security, (e.g. secure exchanges to reduce the constraint of controls in the airports and Train stations improve the safety and security holistic survey approach)

• Smart for Multi-modality, efficient combination of various transport mode according to the expectations of the passenger, an "Unique electronic ticket » for the whole journey…, the environmental issues…

• Smart for Mobility as a service, new set of services for the traveller journey, new services to facilitate the traveller's empowerment and autonomy. Beyond traditional transport modes organized by operators, additional services indeed provide travellers with means to organise, control and optimise their own door-to-door mobility and to turn their travel time into useful or even enjoyable time. Passenger health and concern are follow-up during the journey.

The document will start by set a panel of the main references of what have been done in term of passenger Centric Transportation documentation, then the Smart4Pax concept is presented by its 4 pillars, a specific emphasis is afterward given to the data sharing as backbone of the concept. Then the conclusion will open the floor for the next steps and future initiatives.

PASSENGER CENTRIC APPROACH

The passenger centric approach has been pushed since the last 20 years: ENAC after the project Meta CDM http://www.meta-cdm.org in 2013 push the approach of putting the passenger in the center of the decisionmaking process for the operations. IATA and ACI set up the NEXTT initiative [4], IATA published the StB Simplifying the Business in 2017 [START_REF]IATA published the Simplifying the Business[END_REF], Amadeous Passenger first -2014 [START_REF]Amadeous -Passengers first: Re-thinking irregular operations www[END_REF], Sabre published a passenger centric approach for the airports [START_REF]The Customer-Centric Airport -Redefining The Airport Customer Experience -SABRE-2016[END_REF],

The Mobility as a Service concept have been pushed since 2014 and gives the floor for a set of Initiatives funded by the European Commission and allowing to push the concept of putting the passenger in the centre of the decision-making process, http://www.maas4eu.eu, https://maas-alliance.eu

But what is the reality of our transportation sector in these approaches today. The progression in the cities starts to be visible; there is more and more means to offer new services to the citizens in term of mobility. Bikes, Cars, Public transportation, trains, buses…. But we should notice that there is still gaps between the various modes of transportation in term of cooperation and effective offers of smart solutions to the mobility concerns.

The EU Transport Commissioner Violeta Bulc has called for 2018 to be the "Year of Multimodality" in order to reach the goals set by the Paris agreement in term of sustainable development and to ensure the European transport is safe and our industry remain competitive on the global market. https://ec.europa.eu/transport/themes/logistics-and-multimodal-transport/2018-year-multimodality_en

Ground transports have fostered their collaborations during that year. But air transportation got difficulties to follow this trend. The patchwork of responsibilities for the various Air National Service Providers, the various airports responsibilities, make the collaboration process difficult to address. At the level of the Network Manager of Eurocontrol, the approach remain flight centric and not passenger centric [START_REF][END_REF]. The information on the passenger flow is known at the level of airlines but not at level of the network. It seems obvious that there is a need in order to have means to provide the right level of services to the passengers and to have an efficient passenger centric transportation to collect information and set up new services for the passengers. Some initiatives have been set at airports in collaboration with the cities, but that does not reach the network level yet.

SMART4PAX -PILLARS

SMART FOR ENHANCING PASSENGER EXPERIENCE,

The idea in this approach is to set up a new relation to the journey putting the passenger in the centre of the decision-making process, taking into account its profiles its needs and expectations.

In human societies, mobility is increasingly understood in terms of social links' creation, opportunities and synergies rather than pure problem of travelled distance. The Human mobility becomes individual in the sense where each one conceives his own "mobility cocktail". Changes in individual preferences, behaviors and lifestyles together with innovative Information Communication Technologies (ICT) are likely to impact the way travelers plan their mobility behavior and value their own mobility choices. This mobility behavior and choices concern all the different transport modes that have to be used to perform the full trip. Consideration about the type of passengers and its concerns are crucial in this approach. It is important to consider the constraints such as families traveling with young kids, the elderly having some medical or mobility concerns, persons with reduce mobility, persons having issues to read and understand different languages, persons with medical concerns, business travelers that would like to have some time to work during their travel time. Passenger health and wellbeing are also to be carefully followed during its journey.

An interesting approach was pushed by Nathalie Lenoir and Isabelle Laplace [9], that illustrates the new paradigms for the relation of the traveler to the duration of the travel from door to door. The notion of useful time and lost time are introduced creating a new way to measure the travel duration.

The notion of stress should be addressed specifically. The passengers are under permanent stress on reaching their next connection, from their departure up to their final destination. There are really concerned by airports or station access time, control and checking process duration, disruption of various type as strikes, unexpected unavailability of a mode of transportation between two places, the transit in an airport or station that could take more longer than foreseen, the list could be very long…. This passengers stress is really impacting the passenger experience and also reduce the possibilities to use effectively the travel time for more valuable activities than running from one point to the other or waiting in a room for hours without being able to do anything else than siting (or even standing up) for un unknow duration.

New key indicators of the passenger experience should be put in place, in order to measure how the transportation sector answer to the passenger expectations. It is really by these means that various facilities and mode of operation will be probably improved.

SMART FOR SAFETY AND SECURITY,

The safety is key for the transportation sector, it was always important for all transportation modes to ensure the right level of safety to the travelers. When of the main issue to address in that context, is the number of persons in a given place and the means to evacuate in case of emergency. The fire, in a suburb of Athens ( 2018) is one of the worst cases that we faced in the past years. The safety-first issue should push the transportation means to be efficient and agile to the possible disruptions.

Safety is addressed actually in silos, and the cooperation is only performed around airports, stations, cities in the best cases. This challenge is not easy to address without a level of cooperation between the various mode of transportation. The objective to ensure a safe journey should be consider from the start up to its ends without discontinuities.

Security aspects are even more difficult to address, the transportation system should be able to be resilient and resistant to possible attacks. The security at the stations or airports could be improved by sharing key information on the passengers crossing the various critical areas. A smart survey with units sharing information are keys to ensure a better level of security. Secure exchanges on key passengers' information could reduce the constraints of controls in the airports and train stations, and it would improve the holistic approach on safety and security survey.

SMART FOR MULTI-MODALITY,

This mobility behavior and choices concern all the different transport modes that have to be used to perform the full trip, the door-to-door travel is therefore intrinsically multimodal. Multimodality presupposes the availability of a large range of transport options and various combinations of them that can transfer passengers from their origin to their destination. One stake of multimodality is the ease to change scale, for instance when traveling successively by air with an intercontinental flight and by route with bus or car. Communication technologies and data sharing henceforth are essential part of transport systems. This multi-modality should allow the passenger to make choice according to his expectation and preferences. Environmental issues should be considered and presented to the passenger for helping him to take it into account in his traveling choices. The single ticketing is an approach that will simplify drastically the journey for the travelers. But it should be addressed by all the mode of transport involved in the journey. That could not be achieved without breaking the walls of communication and exchanges between them and without sharing the key information.

The resilience of the transport system is based on the cooperation between the various modes of transports for facing a disruption. An airport closure, a congestion between two cities could definitely be addressed by using the combination of various mode of transportation. Another challenge of the coming year if the capacity to face the expected demand in term of travels(for aviation [START_REF]European Aviation in 2040 Challenges of Growth -EUROCONTROL[END_REF]). This demand capacity balancing could not be solved without a cooperation of the various mode of transport to face it. (Mykoniatis,G. et al [START_REF] Mykoniatis | Demand Capacity Balancing in multi-modal transportation through optimization and simulation -EPSMO 2019 First A[END_REF].). Therefore, coordination and cooperation between the various transport networks are key to address the future demand, safety, disruption, environmental issues of the transport system.

SMART FOR MOBILITY AS A SERVICE,

New set of services for the traveller journey, new services to facilitate the traveller's empowerment and autonomy. Beyond traditional transport modes organized by operators, additional services indeed provide travellers with means to organise, control and optimise their own door-to-door mobility and to turn their travel time into useful or even enjoyable time. These services add value to the travelers' mobility space-time and may change their perception of travel time. Along the way these services can impact travelers' mobility choices [START_REF] Lenoir | Beyond traditional value-of-time: passenger behavior for multi-modal door-to-door travels in the age of information technologies[END_REF].

DATA SHARING THE BACKBONE TO THE FUTURE TRANSPORTATION SYSTEM

For addressing the challenges of the future transportation, the secure sharing of information is essential, It is information shared between the various modes of transport, their relevant network of operation, information their various operational teams, all the security teams involved in the path of the trips, but also, it is information to share with the passengers, and between them. Means to exchange information between the user of the transportation modes is crucial to get a vision of the situation in time. "Waze" approach on the ground transportation could be a good approach to be extended to various mode of transportation.

The security concerns need to get information from the passengers in the various areas they are crossing, to get this crucial information is demand of the willingness of the passenger to share it with the authority or publicly.

This issue could perhaps be addressed by new services or facilities that could be offered to the passengers accepting to share their information; by example a simplified security check or a set of customize services according to passenger's profile during his journey, tailored health survey, better forecast so less stress….

This paper set the scene of the current situation and generates some thought about what could drive new initiatives for addressing the challenge of the passenger centric transportation. It introduces the concept of SMART4PAX with its four pillars:

• Smart for Enhancing passenger experience, new relation to the journey putting the passenger in the centre of the decision-making process, taking into account its profiles its needs and expectations.

• Smart for Safety and security, (e.g. secure exchanges to reduce the constraint of controls in the airports and Train stations improve the safety and security holistic survey approach)

• Smart for Multi-modality, efficient combination of various transport mode according to the expectations of the passenger, an "Unique electronic ticket » for the whole journey…, the environmental issues…

• Smart for Mobility as a service, new set of services for the traveller journey, new services to facilitate the traveller's empowerment and autonomy. Beyond traditional transport modes organized by operators, additional services indeed provide travellers with means to organise, control and optimise their own door-to-door mobility and to turn their travel time into useful or even enjoyable time. Passenger health and concern are follow-up during the journey.

The information sharing appears to be the backbone of the SMART4PAX concept. The future challenge of passenger centered transportation push the various mode of transportation to share and collaborate in order to offer safe, efficient, enjoyable journey to the passengers.

First A .

 . Author, Second B. Author, and Third C. Coauthor.

  The Object of this paper, is to set the scene on the current situation and generate some thought about what could drive new initiatives for addressing the challenge of the passenger centric transportation. It takes its roots from what have been published by the main institutions (Eurocontrol, IACAO, IATA, ACI…) and partners on the subject. This document is based also on a proposition Motivate (MulTimodal VAlue of TimE -List of participants: ENAC : Isabelle Laplace, VTM Consultore: Sofia Kalakou, Stiftelsen SINTEF: Trond Bakken, ITS Norway: Trond Hovland, Instituto Superior Tecnico: Filipe Moura ) and exchanges during its preparation. This document has also as objective to set the scene of the SMART4PAX concept.


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