Pose Estimation of Non-Cooperative Target Coated With MLI

The relative pose estimation between the chaser and the non-cooperative target is a significant prerequisite for performing on-orbit servicing (OOS) missions. The chaser could design the close-range rendezvous trajectory to approach the non-cooperative target only when the relative pose parameters are obtained effectively and efficiently. In this paper, a pose estimation scheme is designed to obtain the relative pose parameters between the chaser and the non-cooperative target coated with multilayer insulation material (MLI). The scheme utilizes a time-of-flight (TOF) camera to acquire 3D point clouds of the non-cooperative target, and uses the iterative closest point (ICP) method to achieve the point cloud registration between every two frames. Aiming at decreasing the non-systematic errors caused by MLI in the data acquisition of the TOF camera, the corresponding point median filtering approach is adopted to filter out the bad corresponding point pairs generated in the ICP method. A semi-physical experiment is carried out to evaluate the performance of the proposed scheme, the result of which shows that the proposed scheme could notably improve the accuracy of pose estimation of the non-cooperative target, meanwhile the computational efficiency is also ensured.

[1]  Lei Li,et al.  A new pose estimation method for non-cooperative spacecraft based on point cloud , 2019, Int. J. Intell. Comput. Cybern..

[2]  Yuming Bo,et al.  Point Cloud Based Relative Pose Estimation of a Satellite in Close Range , 2016, Sensors.

[3]  Patrick Rives,et al.  An Efficient Direct Approach to Visual SLAM , 2008, IEEE Transactions on Robotics.

[4]  Andreas Fleischner,et al.  Pose Tracking of a Noncooperative Spacecraft During Docking Maneuvers Using a Time-of-Flight Sensor , 2016 .

[5]  Michèle Lavagna,et al.  Stereovision-based pose and inertia estimation of unknown and uncooperative space objects , 2017 .

[6]  Klaus Schilling,et al.  On-line robust pose estimation for Rendezvous and Docking in space using photonic mixer devices ☆ , 2014 .

[7]  C. English,et al.  Real-Time Dynamic Pose Estimation Systems in Space : Lessons Learned for System Design and Performance Evaluation , 2011 .

[8]  John A. Christian,et al.  Lidar-based relative navigation with respect to non-cooperative objects , 2016 .

[9]  W. Christian INTEGRATED RANGE CAMERA CALIBRATION USING IMAGE SEQUENCES FROM HAND-HELD OPERATION , 2008 .

[10]  Yuming Bo,et al.  LiDAR-Based Non-Cooperative Tumbling Spacecraft Pose Tracking by Fusing Depth Maps and Point Clouds , 2018, Sensors.

[11]  Yasuhiro Aoki,et al.  PointNetLK: Robust & Efficient Point Cloud Registration Using PointNet , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  S. Foix,et al.  Lock-in Time-of-Flight (ToF) Cameras: A Survey , 2011, IEEE Sensors Journal.

[13]  Roberto Opromolla,et al.  Autonomous relative navigation around uncooperative spacecraft based on a single camera , 2019, Aerospace Science and Technology.

[14]  Roberto Opromolla,et al.  Uncooperative pose estimation with a LIDAR-based system , 2015 .

[15]  Wang Pan,et al.  Rectangular-structure-based pose estimation method for non-cooperative rendezvous. , 2018, Applied optics.

[16]  Roberto Opromolla,et al.  A review of cooperative and uncooperative spacecraft pose determination techniques for close-proximity operations , 2017 .

[17]  Sumant Sharma,et al.  Comparative assessment of techniques for initial pose estimation using monocular vision , 2016 .

[18]  Heike Benninghoff,et al.  Initial Pose Estimation using PMD Sensor during the Rendezvous Phase in On-Orbit Servicing Missions , 2017 .

[19]  Roberto Opromolla,et al.  A Model-Based 3D Template Matching Technique for Pose Acquisition of an Uncooperative Space Object , 2015, Sensors.

[20]  Xiaoping Du,et al.  Scale-unambiguous relative pose estimation of space uncooperative targets based on the fusion of three-dimensional time-of-flight camera and monocular camera , 2015 .

[21]  Marco Lovera,et al.  Comparison of filtering techniques for relative attitude estimation of uncooperative space objects , 2019, Aerospace Science and Technology.

[22]  Nico Blodow,et al.  Towards 3D Point cloud based object maps for household environments , 2008, Robotics Auton. Syst..

[23]  Ou Ma,et al.  A review of space robotics technologies for on-orbit servicing , 2014 .

[24]  Heather M. Rodriguez,et al.  Optical Properties of Multi-Layered Insulation , 2007 .

[25]  Simone D'Amico,et al.  Pose Estimation for Non-Cooperative Rendezvous Using Neural Networks , 2019, ArXiv.

[26]  Zhengyou Zhang,et al.  Iterative point matching for registration of free-form curves and surfaces , 1994, International Journal of Computer Vision.

[27]  Bin Liang,et al.  Non-cooperative spacecraft pose tracking based on point cloud feature , 2017 .

[28]  Klaus Schilling,et al.  Relative pose estimation of satellites using PMD-/CCD-sensor data fusion , 2015 .

[29]  H. Aanaes,et al.  Environmental Effects on Measurement Uncertainties of Time-of-Flight Cameras , 2007, 2007 International Symposium on Signals, Circuits and Systems.

[30]  T. Kahlmann,et al.  Calibration and development for increased accuracy of 3D range imaging cameras , 2008 .

[31]  Heinz Hügli,et al.  Optimized scattering compensation for time-of-flight camera , 2007, SPIE Optics East.

[32]  Tae W. Lim,et al.  Edge Detection Using Point Cloud Data for Noncooperative Pose Estimation , 2017 .

[33]  Bernd Eissfeller,et al.  Pose estimation and tracking of non-cooperative rocket bodies using Time-of-Flight cameras , 2017 .

[34]  Roland Siegwart,et al.  Comparing ICP variants on real-world data sets , 2013, Auton. Robots.

[35]  Scott P. Cryan,et al.  A Survey of LIDAR Technology and Its Use in Spacecraft Relative Navigation , 2013 .

[36]  John A. Christian,et al.  LIDAR-based Relative Navigation of Non-Cooperative Objects Using Point Cloud Descriptors , 2016 .

[37]  J. A. Ware,et al.  Near real-time point cloud processing using the PCL , 2015, 2015 International Conference on Systems, Signals and Image Processing (IWSSIP).

[38]  Klaus Schilling,et al.  Model-based spacecraft pose estimation and motion prediction using a photonic mixer device camera , 2011 .

[39]  Quan Pan,et al.  Pose and motion estimation of unknown tumbling spacecraft using stereoscopic vision , 2018, Advances in Space Research.

[40]  Simone D'Amico,et al.  Pose estimation for non-cooperative spacecraft rendezvous using convolutional neural networks , 2018, 2018 IEEE Aerospace Conference.

[41]  Roberto Opromolla,et al.  Pose Estimation for Spacecraft Relative Navigation Using Model-Based Algorithms , 2017, IEEE Transactions on Aerospace and Electronic Systems.

[42]  Yunpeng Wang,et al.  Using consecutive point clouds for pose and motion estimation of tumbling non-cooperative target , 2019, Advances in Space Research.

[43]  Wolfgang Hess,et al.  Real-time loop closure in 2D LIDAR SLAM , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).