Seminar on From Ground to Orbit: Innovations in Robotic Navigation & Space Situational Awareness
Professor Martin Adams University of Chile, Chile This seminar will be chaired by Prof Chen I-Ming. | ||
| Seminar Abstract | ||
Autonomous navigation, multi-target tracking and data registration are estimation problems with applications ranging from robotic simultaneous localization and mapping (SLAM) to space situational awareness (SSA). While SLAM integrates detected features into dynamic maps, SSA tracks space debris to mitigate collision risks, including the Kessler syndrome. Despite their distinct environments, both applications can be addressed using a common random finite set (RFS)-based multi-object state estimation framework. Key challenges include changing object numbers, noisy measurements, false alarms, missed detections and data association. Bayesian RFS methods model multiple objects as a single random variable, enabling joint estimation of object numbers and states while reducing reliance on fragile data association and object initiation and termination procedures. This presentation introduces new multi-object solutions for SLAM and SSA. For SLAM, a mixed distribution jointly models the trajectory and RFS-valued map, achieving competitive and often superior results without fragile data association. For SSA, finite set statistics (FISST) and Riemannian manifold geometry are used to track multiple space debris in low Earth orbit through telescopic measurements. | ||
| Speaker's Biography | ||
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