Pose estimation of a non-cooperative spacecraft without the detection and recognition of point cloud features

Abstract This paper presents a relative position and attitude estimation method using consecutive point clouds without feature extraction. Using this method, the inaccurate state estimation problems for non-cooperative targets caused by the mismatched point pairs or the low tracking accuracy of point cloud features can be resolved. First, point cloud registration is carried out by the transformation of the covariance matrices of the point cloud between two adjacent frames. Meanwhile, the random sample consensus algorithm is employed to reject the mismatched point pairs. Then, pose-graph optimization is adopted to eliminate the accumulated errors of consecutive point cloud registration. Finally, an Extended Kalman Filter is designed to estimate the position, velocity, and angular velocity of the target. The experimental results show that the covariance matrix transform algorithm can achieve the point cloud registration for close roto-translational motions, and the target motion state can be estimated effectively and continuously.

[1]  Xiaofeng Liu,et al.  Pose Estimation of Non-Cooperative Target Coated With MLI , 2019, IEEE Access.

[2]  Dewi Mutiara Sari,et al.  Optimization Estimating 3D Object Pose Using Levenberg-Marquardt Method , 2019, 2019 International Electronics Symposium (IES).

[3]  Simon Lacroix,et al.  ICP-based pose-graph SLAM , 2016, 2016 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR).

[4]  António Paulo Moreira,et al.  Map-Matching Algorithms for Robot Self-Localization: A Comparison Between Perfect Match, Iterative Closest Point and Normal Distributions Transform , 2018, Journal of Intelligent & Robotic Systems.

[5]  Tae W. Lim,et al.  Detection and Identification of Objects Using Point Cloud Data for Pose Estimation , 2016 .

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

[7]  Peter Biber,et al.  The normal distributions transform: a new approach to laser scan matching , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[8]  Simone D'Amico,et al.  Towards Robust Learning-Based Pose Estimation of Noncooperative Spacecraft , 2019, ArXiv.

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

[10]  Eberhard Gill,et al.  Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft , 2019, Progress in Aerospace Sciences.

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

[12]  Jesus Gil Fernandez,et al.  Comparative Assessment of Image Processing Algorithms for the Pose Estimation of Uncooperative Spacecraft , 2019 .

[13]  Bin Liang,et al.  Pose Measurement and Motion Estimation of Space Non-Cooperative Targets Based on Laser Radar and Stereo-Vision Fusion , 2019, IEEE Sensors Journal.

[14]  Ke Lu,et al.  Accelerated nonrigid image registration using improved Levenberg-Marquardt method , 2018, Inf. Sci..

[15]  H. Abdi,et al.  Principal component analysis , 2010 .

[16]  John J. Leonard,et al.  Factor Graph Modeling of Rigid‐body Dynamics for Localization, Mapping, and Parameter Estimation of a Spinning Object in Space , 2015, J. Field Robotics.

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

[18]  Surekha Kamath,et al.  Review of Active Space Debris Removal Methods , 2019, Space Policy.

[19]  E TweddleBrent,et al.  Factor Graph Modeling of Rigid-body Dynamics for Localization, Mapping, and Parameter Estimation of a Spinning Object in Space , 2015 .

[20]  AbdiHervé,et al.  Principal Component Analysis , 2010, Essentials of Pattern Recognition.

[21]  Manoranjan Majji,et al.  Bayesian Inference of Spacecraft Pose using Particle Filtering , 2019, ArXiv.

[22]  Roberto Opromolla,et al.  Large space debris pose acquisition in close-proximity operations , 2015, 2015 IEEE Metrology for Aerospace (MetroAeroSpace).

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

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

[25]  W. H. Clohessy,et al.  Terminal Guidance System for Satellite Rendezvous , 2012 .

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

[27]  David K. Geller,et al.  Navigating the Road to Autonomous Orbital Rendezvous , 2007 .

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

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

[30]  Arun Das,et al.  3D scan registration using the Normal Distributions Transform with ground segmentation and point cloud clustering , 2013, 2013 IEEE International Conference on Robotics and Automation.

[31]  Shao-Wen Yang,et al.  RANSAC matching: Simultaneous registration and segmentation , 2010, 2010 IEEE International Conference on Robotics and Automation.

[32]  Andreas Uhl,et al.  BlenSor: Blender Sensor Simulation Toolbox , 2011, ISVC.

[33]  David L. Swift,et al.  Application of both a physical theory and statistical procedure in the analyses of an in vivo study of aerosol deposition , 1995 .

[34]  John A. Christian,et al.  Comparison of Orion Vision Navigation Sensor Performance from STS-134 and the Space Operations Simulation Center , 2012 .

[35]  Paul J. Besl,et al.  Method for registration of 3-D shapes , 1992, Other Conferences.