Abstract-Autonomous rendezvous and docking is necessary for planned space programs such as DARPA ASTRO, NASA MSR, ISS assembly and servicing, and other rendezvous and proximity operations. Estimation of the relative pose between the host platform and a resident space object is a critical ability. We present a model-based pose refinement algorithm, part of a suite of algorithms for vision-based relative pose estimation and tracking. Algorithms were tested in highfidelity simulation and stereo-vision hardware testbed environments. Testing indicated that in most cases, the modelbased pose refinement algorithm can handle initial attitude errors up to about 20 degrees, range errors exceeding 10% of range, and transverse errors up to about 2% of range. Preliminary point tests with real camera sequences of a 1/24 scale Magellan satellite model using a simple fixed-gain tracking filter showed potential tracking performance with mean errors of < 3 degrees and < 2% of range.

This paper presents the Visual Threat Awareness (VISTA) system for real time collision obstacle detection for an unmanned air vehicle (UAV). Computational stereo performance has progressed such that several commercial or open source implementations are available which operate at frame rate, but suffer from well known correspondence errors. We show that introducing a global segmentation step after commodity stereo can increase robustness and leverage existing stereo software. The global segmentation step is based on a graph structure appropriate for collision detection, human vision inspired foveation, perceptual organization and graph partitioning using the minimum s-t graph cut. This system has been prototyped using the Sarnoff Acadia I vision processor to enable processing of 640x480 resolution imagery at 5-10Hz operation on embedded avionics. We describe system theory, demonstrate segmentation results on scenes of increasing complexity, and show flight experiment results on Georgia Tech's GT-Max autonomous helicopter against real collision obstacles.

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