30 September 2003 Estimation of state, shape, and inertial parameters of space objects from sequences of range images
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Proceedings Volume 5267, Intelligent Robots and Computer Vision XXI: Algorithms, Techniques, and Active Vision; (2003) https://doi.org/10.1117/12.514813
Event: Photonics Technologies for Robotics, Automation, and Manufacturing, 2003, Providence, RI, United States
Abstract
This paper presents an architecture for the estimation of dynamic state, geometric shape, and inertial parameters of objects in orbit, using on-orbit cooperative 3-D vision sensors. This has application in many current and projected space missions, such as satellite capture and servicing, debris capture and mitigation, and large space structure assembly and maintenance. The method presented here consists of three distinct parts: (1) kinematic data fusion, which condenses sensory data into a coarse estimate of target pose; (2) Kalman filtering, which filters these coarse estimates and extracts the full dynamic state and inertial parameters of the target; and (3) shape estimation, which uses filtered pose information and the raw sensory data to build a probabilistic map of the target’s shape. This method does not rely on feature detection, optical flow, or model matching, and therefore is robust to the harsh sensing conditions of space. Instead, it exploits the well-modeled dynamics of objects in space through the Kalman filter. The architecture is computationally fast since only coarse measurements need to be provided to the Kalman filter. This paper will summarize the three steps of the architecture. Simulation results will follow showing the theoretical performance of the architecture.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Matthew D Lichter, Matthew D Lichter, Steven Dubowsky, Steven Dubowsky, } "Estimation of state, shape, and inertial parameters of space objects from sequences of range images", Proc. SPIE 5267, Intelligent Robots and Computer Vision XXI: Algorithms, Techniques, and Active Vision, (30 September 2003); doi: 10.1117/12.514813; https://doi.org/10.1117/12.514813
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