Motion and Parameter Estimation of Space Objects Using Laser-Vision Data

free-falling tumbling satellite (target). The filter receives only noisy pose measurements from a laser-vision system aboard another satellite (chaser) at a close distance in a neighboring orbit. The filter estimates the full states, all the inertia parameters of the target satellite, and the covariance of the measurement noise. A comprehensive dynamics model that includes aspects of orbital mechanics is incorporated for accurate estimation. The discrete-time model, whichinvolvesastate-transitionmatrixandthecovarianceofprocessnoise,isderivedinclosedform,thusrendering the filtersuitableforreal-timeimplementation.Thestatisticalcharacteristicsofthemeasurementnoiseisformulated by a state-dependent covariance matrix. This model allows additive quaternion noise, while preserving the unitnorm property of the quaternion. The convergence properties of the developed filter is demonstrated by simulation andexperimental results. These results also demonstrate that the filter can continuously produce accurate estimates of pose even when the vision system is occluded for tens of seconds.

[1]  Galina Okouneva,et al.  Fault-tolerant pose estimation of space objects , 2010, 2010 IEEE/ASME International Conference on Advanced Intelligent Mechatronics.

[2]  Son-Goo Kim,et al.  Kalman filtering for relative spacecraft attitude and position estimation , 2005 .

[3]  F Aghili,et al.  Fault-Tolerant Position/Attitude Estimation of Free-Floating Space Objects Using a Laser Range Sensor , 2011, IEEE Sensors Journal.

[4]  Michael A. Greenspan,et al.  Discrete pose space estimation to improve ICP-based tracking , 2005, Fifth International Conference on 3-D Digital Imaging and Modeling (3DIM'05).

[5]  Galina Okouneva,et al.  Robust vision-based pose estimation of moving objects for Automated Rendezvous & Docking , 2010, 2010 IEEE International Conference on Mechatronics and Automation.

[6]  Ulrich Hillenbrand,et al.  Motion and Parameter Estimation of a Free-Floating Space Object from Range Data for Motion Prediction , 2005 .

[7]  R. Mehra On the identification of variances and adaptive Kalman filtering , 1970 .

[8]  Beno Benhabib,et al.  Optimal rendezvous-point selection for robotic interception of moving objects , 1998, IEEE Trans. Syst. Man Cybern. Part B.

[9]  Adam Deslauriers,et al.  The complementary nature of triangulation and ladar technologies , 2005, SPIE Defense + Commercial Sensing.

[10]  Yasuhiro Masutani,et al.  Motion estimation of unknown rigid body under no external forces and moments , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[11]  Farhad Aghili,et al.  Adaptive motion estimation of a tumbling satellite using laser-vision data with unknown noise characteristics , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[12]  Stephane Ruel,et al.  3D LASSO: REAL-TIME POSE ESTIMATION FROM 3D DATA FOR AUTONOMOUS SATELLITE SERVICING , 2005 .

[13]  C. Loan Computing integrals involving the matrix exponential , 1978 .

[14]  Gianfranco Visentin,et al.  Robotics for geostationary satellite servicing , 1998, Robotics Auton. Syst..

[15]  Rajeev Sharma,et al.  Dynamic robot manipulation using visual tracking , 1992, Proceedings 1992 IEEE International Conference on Robotics and Automation.

[16]  Robert M. Sanner,et al.  Hubble Space Telescope Angular Velocity Estimation During the Robotic Servicing Mission , 2005 .

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

[18]  Mark E. Pittelkau,et al.  Kalman Filtering for Spacecraft System Alignment Calibration , 2001 .

[19]  Kazuya Yoshida,et al.  Engineering Test Satellite VII Flight Experiments for Space Robot Dynamics and Control: Theories on Laboratory Test Beds Ten Years Ago, Now in Orbit , 2003, Int. J. Robotics Res..

[20]  Francois Blais,et al.  The Neptec Three-Dimensional Laser Camera System: From Space Mission STS-105 to Terrestrial Applications , 2004 .

[21]  Yoshiaki Ohkami,et al.  SPACE ROBOTIC MISSION CONCEPTS FOR CAPTURING STRAY OBJECTS , 2002 .

[22]  Guanrong Chen,et al.  Kalman Filtering with Real-time Applications , 1987 .

[23]  Steven Dubowsky,et al.  State, shape, and parameter estimation of space objects from range images , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[24]  James C. Wilcox,et al.  A New Algorithm for Strapped-Down Inertial Navigation , 1967, IEEE Transactions on Aerospace and Electronic Systems.

[25]  T. Kailath,et al.  An innovations approach to least-squares estimation--Part II: Linear smoothing in additive white noise , 1968 .

[26]  Beno Benhabib,et al.  Robotic interception of moving objects using an augmented ideal proportional navigation guidance technique , 2000, IEEE Trans. Syst. Man Cybern. Part A.

[27]  Y. Chen,et al.  Optimal trajectory planning for a space robot docking with a moving target via homotopy algorithms , 1995, J. Field Robotics.

[28]  Francois Blais,et al.  Neptec 3D Laser Camera System: from space mission STS-105 to terrestrial applications , 2002 .

[29]  M. Kaplan Modern Spacecraft Dynamics and Control , 1976 .

[30]  E. J. Lefferts,et al.  Kalman Filtering for Spacecraft Attitude Estimation , 1982 .