Nanosatellite maneuver planning for point cloud generation with a rangefinder

This paper discusses the application of a single beam laser rangefinder (LRF) to point cloud generation, shape detection, and shape reconstruction for a space-based space situational awareness (SSA) mission. The LRF is part of the payload of a chaser satellite tasked to image a resident space object (RSO). The one-dimensional (1D) nature of LRF returns significantly increases the complexity of the imaging task. To maximize coverage, a method to autonomously detect and fill gaps in sparse point cloud coverage using a narrow field of view (NFOV) camera in conjunction with the LRF is presented. First, relative orbital motion and scanning attitude motion are combined to generate a baseline 3D point cloud of the RSO. The effectiveness of pregenerated command profiles is analyzed by using a weighted edge reconstruction metric that scores how well a point cloud characterizes RSO shape. The design and characterization of multiple relative motion and attitude maneuver profiles, as well as the time and propellant cost of each profile, are presented with the assumption that the entire metrology chain is error free. Next, a three-part algorithm is used that creates a 3D panoramic map from stitched NFOV camera images, correlates the areas of sparse LRF coverage to the map, and generates attitude commands to close the coverage. This provides a consistent and reliable method to register positions of strike points relative to each other and to the NFOV image of the RSO with a priori knowledge of the RSO attitude. Gaps and sparse areas in LRF coverage are covered with strike points; the result is a point cloud of significantly higher resolution than that obtained with preprogrammed attitude profiles. The analysis bears particular relevance to power-constrained nanosatellite missions for space-based SSA for whom a multibeam LRF payload is not feasible. Maneuvers can now be designed on-line in real time; results presented validate the utility of a single-beam LRF as a tool for 3D imaging of RSO shapes.