This paper expands on previous studies by the authors into 3D imaging with a single-beam laser rangefinder (LRF) by implementing real-time attitude maneuvers of a chaser satellite flying in relative orbit around a resident space object (RSO). Point clouds generated with an LRF are much sparser than those generated with an imaging LIDAR, making it difficult to autonomously distinguish between gaps in coverage and truly empty space. Furthermore, if both the attitude and the shape of the target RSO are unknown, it is particularly difficult to register a collection of LRF strike points together and detect gaps in strike point coverage in realtime. This paper presents the incorporation of a narrow field of-view (NFOV) camera that detects the strike point on the RSO and supplements LRF distance measurements with image data. This data is used to generate attitude command profiles that efficiently fill LRF coverage gaps and generate high density point clouds, thus maximizing coverage of an unknown RSO. Results obtained so far point the way to a real-time implementation of the algorithm. A method to detect and close gaps in LRF strike point coverage is presented first. Coverage gap detection is achieved using Voronoi diagrams, where Voronoi cells are centered at the LRF strike points. A three-part algorithm is used that 1) creates a 3D panoramic map from “stitched” NFOV camera images; 2) correlates the areas of sparse LRF coverage to the map; and 3) generates attitude commands to close the coverage gaps. The map provides a consistent and reliable method to register positions of strike points relative to each other and to the NFOV image of the RSO without a priori knowledge of the RSO attitude. Using this algorithm, gaps and sparse areas in LRF coverage are covered with strike points, allowing for the generation of a higher-resolution point cloud than that obtained with preprogrammed attitude profiles. Attitude maneuvers can now be designed on-line in real-time such that they satisfy the constraints of the chaser spacecraft attitude determination and control system. Finally, the effectiveness of the camera-aided generation of attitude profiles is analyzed by using a weighted edge reconstruction metric, and comparing results to those generated with pre-programmed attitude maneuvers. The effect of on-line maneuver generation on the overall decrease of time and propellant expenditure to generate an adequate point cloud is also discussed. The analysis bears particular relevance to low-budget, nano-satellite demonstration missions for space-based space situational awareness (SSA).
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