Robust and Dynamics-Aware Active Perception for Space Robotics

This research advances the state-of-the-art in computer vision and perception for space applications by addressing three critical limitations: first, existing feature detection methods struggle with the harsh illumination conditions of space; second, current 3D reconstruction techniques fail to account for the dynamic orbital environment; and third, traditional simultaneous localization and mapping (SLAM) approaches cannot handle the diverse motion regimes of resident space objects (RSOs).

To address these challenges, the research team develops a robust learning-based feature detection framework for space by using line features that exploit the inherent geometry of the target, along with multi-spectral imaging feature extraction. It also develops a novel dynamics-aware 3D Gaussian Splatting framework that incorporates relative orbital dynamics as physical constraints, enabling simultaneous state estimation and motion regime classification while maintaining physical consistency. The research team introduces a visibility-aware neural field representation that explicitly models observation uncertainty to drive information-theoretic view planning, thereby enabling autonomous space robots to systematically explore unknown objects through efficient observation sequences.

Finally, the developed theory is experimentally tested and validated using Georgia Tech’s Autonomous Spacecraft Testing of Robotic Operations in Space (ASTROS) platform, a state-of-the-art spacecraft simulation platform, and also using a high-fidelity synthetic simulation environment.

Sponsors

This project is supported by NSF.