3D sensor algorithms for spacecraft pose determination

John M. Trenkle; Peter Tchoryk Jr.; Greg A. Ritter; Jane C. Pavlich; Aaron S. Hickerson

doi:10.1117/12.669260

30 May 2006 3D sensor algorithms for spacecraft pose determination

John M. Trenkle, Peter Tchoryk Jr., Greg A. Ritter, Jane C. Pavlich, Aaron S. Hickerson

Proceedings Volume 6220, Spaceborne Sensors III; 62200D (2006) https://doi.org/10.1117/12.669260
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

Abstract

Researchers at the Michigan Aerospace Corporation have developed accurate and robust 3-D algorithms for pose determination (position and orientation) of satellites as part of an on-going effort supporting autonomous rendezvous, docking and space situational awareness activities. 3-D range data from a LAser Detection And Ranging (LADAR) sensor is the expected input; however, the approach is unique in that the algorithms are designed to be sensor independent. Parameterized inputs allow the algorithms to be readily adapted to any sensor of opportunity. The cornerstone of our approach is the ability to simulate realistic range data that may be tailored to the specifications of any sensor. We were able to modify an open-source raytracing package to produce point cloud information from which high-fidelity simulated range images are generated. The assumptions made in our experimentation are as follows: 1) we have access to a CAD model of the target including information about the surface scattering and reflection characteristics of the components; 2) the satellite of interest may appear at any 3-D attitude; 3) the target is not necessarily rigid, but does have a limited number of configurations; and, 4) the target is not obscured in any way and is the only object in the field of view of the sensor. Our pose estimation approach then involves rendering a large number of exemplars (100k to 5M), extracting 2-D (silhouette- and projection-based) and 3-D (surface-based) features, and then training ensembles of decision trees to predict: a) the 4-D regions on a unit hypersphere into which the unit quaternion that represents the vehicle [Q_X, Q_Y, Q_Z, Q_W] is pointing, and, b) the components of that unit quaternion. Results have been quite promising and the tools and simulation environment developed for this application may also be applied to non-cooperative spacecraft operations, Autonomous Hazard Detection and Avoidance (AHDA) for landing craft, terrain mapping, vehicle guidance, path planning and obstacle avoidance.

Citation Download Citation

John M. Trenkle, Peter Tchoryk Jr., Greg A. Ritter, Jane C. Pavlich, and Aaron S. Hickerson "3D sensor algorithms for spacecraft pose determination", Proc. SPIE 6220, Spaceborne Sensors III, 62200D (30 May 2006); https://doi.org/10.1117/12.669260

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