🤖 AI Summary
This work addresses the challenge of achieving robust real-time visual odometry for planetary rovers under extreme illumination conditions and stringent computational constraints. The authors propose a novel monocular visual odometry method leveraging an event camera, which uniquely integrates asynchronous event streams with an Error-State Kalman Filter (ESKF) and incorporates the real-time asynchronous feature tracker RATE. This integration enables low-bandwidth, highly efficient ego-motion estimation. The approach maintains stable performance across high dynamic range lighting and complex terrains, significantly enhancing both the robustness and real-time capability of pose estimation while meeting the practical resource limitations inherent to planetary rover platforms.
📝 Abstract
We describe our preliminary design of a real-time asynchronous event-based monocular odometry for planetary exploration. Operating under strict computational constraints, planetary rovers frequently encounter complex, unpredictable environments that demand high-speed sensing and robustness to high dynamic range (HDR) lighting. Event cameras address these needs by reporting asynchronous, pixel-wise brightness changes with microsecond resolution, significantly reducing data bandwidth while maintaining robustness in extreme lighting conditions. We propose an approach based on an Error-State Kalman Filter (ESKF) that leverages this asynchronous event stream to continuously estimate camera ego-motion. The camera state is updated with every tracked position output generated by RATE, a real-time asynchronous feature tracker.