Stereovision-based pose and inertia estimation of unknown and uncooperative space objects

Abstract Autonomous close proximity operations are an arduous and attractive problem in space mission design. In particular, the estimation of pose, motion and inertia properties of an uncooperative object is a challenging task because of the lack of available a priori information. This paper develops a novel method to estimate the relative position, velocity, angular velocity, attitude and the ratios of the components of the inertia matrix of an uncooperative space object using only stereo-vision measurements. The classical Extended Kalman Filter (EKF) and an Iterated Extended Kalman Filter (IEKF) are used and compared for the estimation procedure. In addition, in order to compute the inertia properties, the ratios of the inertia components are added to the state and a pseudo-measurement equation is considered in the observation model. The relative simplicity of the proposed algorithm could be suitable for an online implementation for real applications. The developed algorithm is validated by numerical simulations in MATLAB using different initial conditions and uncertainty levels. The goal of the simulations is to verify the accuracy and robustness of the proposed estimation algorithm. The obtained results show satisfactory convergence of estimation errors for all the considered quantities. The obtained results, in several simulations, shows some improvements with respect to similar works, which deal with the same problem, present in literature. In addition, a video processing procedure is presented to reconstruct the geometrical properties of a body using cameras. This inertia reconstruction algorithm has been experimentally validated at the ADAMUS (ADvanced Autonomous MUltiple Spacecraft) Lab at the University of Florida. In the future, this different method could be integrated to the inertia ratios estimator to have a complete tool for mass properties recognition.

[1]  Kim D. Listmann,et al.  A comparison of methods for higher-order numerical differentiation , 2013, 2013 European Control Conference (ECC).

[2]  P. Gurfil,et al.  Manifolds and Metrics in the Relative Spacecraft Motion Problem , 2006 .

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

[4]  Peter S. Maybeck,et al.  Stochastic Models, Estimation And Control , 2012 .

[5]  L. A. Mcgee,et al.  Discovery of the Kalman filter as a practical tool for aerospace and industry , 1985 .

[6]  Steven Dubowsky,et al.  Shape, Motion, and Parameter Estimation of Large Flexible Space Structures using Range Images , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[7]  P. Perona,et al.  Motion estimation via dynamic vision , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[8]  R. C. Fenton,et al.  A Ladar-Based Pose Estimation Algorithm for Determining Relative Motion of a Spacecraft for Autonomous Rendezvous and Dock , 2008 .

[9]  Brent E. Tweddle,et al.  Computer vision-based localization and mapping of an unknown, uncooperative and spinning target for spacecraft proximity operations , 2013 .

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

[11]  Allan D. Jepson,et al.  Subspace methods for recovering rigid motion I: Algorithm and implementation , 2004, International Journal of Computer Vision.

[12]  John Leif Jørgensen,et al.  Pose estimation of an uncooperative spacecraft from actual space imagery , 2014 .

[13]  Gangqi Dong,et al.  Autonomous robotic capture of non-cooperative target by adaptive extended Kalman filter based visual servo , 2016 .

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

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

[16]  N. Qian.  Binocular Disparity and the Perception of Depth , 1997, Neuron.

[17]  Tiago Soares,et al.  The e.Deorbit CDF Study: A Design Study for the Safe Removal of a Large Space Debris , 2013 .

[18]  F. Markley,et al.  Quaternion Attitude Estimation Using Vector Observations , 2000 .

[19]  Alvar Saenz-Otero,et al.  Engineering Notes Relative Computer Vision-Based Navigation for Small Inspection Spacecraft , 2015 .

[20]  Overview of the DART Mishap Investigation Results For Public Release , 2006 .

[21]  N. K. Philip,et al.  Relative position and attitude estimation and control schemes for the final phase of an autonomous docking mission of spacecraft , 2003 .

[22]  Frank Dellaert,et al.  iSAM: Fast Incremental Smoothing and Mapping with Efficient Data Association , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[23]  Salah Sukkarieh,et al.  Building a Robust Implementation of Bearing‐only Inertial SLAM for a UAV , 2007, J. Field Robotics.

[24]  Hauke Strasdat,et al.  Real-time monocular SLAM: Why filter? , 2010, 2010 IEEE International Conference on Robotics and Automation.

[25]  Galina Okouneva,et al.  Intelligent LIDAR scanning region selection for satellite pose estimation , 2007, Comput. Vis. Image Underst..

[26]  R. Landis,et al.  AIDA: The Asteroid Impact & Deflection Assessment Mission , 2013 .

[27]  Frank Dellaert,et al.  iSAM: Incremental Smoothing and Mapping , 2008, IEEE Transactions on Robotics.

[28]  Javier Civera,et al.  EKF monocular SLAM with relocalization for laparoscopic sequences , 2011, 2011 IEEE International Conference on Robotics and Automation.

[29]  P. Perona,et al.  Recursive 3-D Visual Motion Estimation Using Subspace Constraints , 1997, International Journal of Computer Vision.

[30]  Paolo Cignoni,et al.  MeshLab: an Open-Source Mesh Processing Tool , 2008, Eurographics Italian Chapter Conference.

[31]  Giovanni B. Palmerini,et al.  Adaptive Thrust Vector Control during On-Orbit Servicing , 2014 .

[32]  S. Debei,et al.  OSIRIS – The Scientific Camera System Onboard Rosetta , 2007 .

[33]  David J. Fleet,et al.  Performance of optical flow techniques , 1994, International Journal of Computer Vision.

[34]  Robert B. Friend,et al.  Orbital Express program summary and mission overview , 2008, SPIE Defense + Commercial Sensing.

[35]  Shai Segal,et al.  Stereovision-Based Estimation of Relative Dynamics Between Noncooperative Satellites: Theory and Experiments , 2014, IEEE Transactions on Control Systems Technology.

[36]  Gangqi Dong,et al.  Incremental inverse kinematics based vision servo for autonomous robotic capture of non-cooperative space debris , 2016 .

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

[38]  Shai Segal,et al.  Vision-based relative state estimation of non-cooperative spacecraft under modeling uncertainty , 2011, 2011 Aerospace Conference.

[39]  P. Gurfil,et al.  Effect of Kinematic Rotation-Translation Coupling on Relative Spacecraft Translational Dynamics , 2009 .

[40]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[41]  Heike Benninghoff,et al.  Rendezvous Involving a Non-Cooperative, Tumbling Target - Estimation of Moments of Inertia and Center of Mass of an Unknown Target , 2015 .

[42]  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.