Recurrent Vision Transformers for Event-Based Spacecraft Pose Estimation Under Slow Relative Motions
Abstract: Vision-based object pose estimation is vital for conducting spacecraft rendezvous and proximity operations (RPO). Recent research has demonstrated the robustness of event cameras against the harsh lighting conditions that often occur in orbit, thanks to the extremely high dynamic range of event sensors. However, event-based spacecraft pose estimation (EBSPE) suffers when the relative motion between the chaser and target is slow, since the event rate drops significantly, leading to data with low signal-to-noise ratio. Unfortunately, for safety reasons, real RPO trajectories often involve slow relative motions (e.g., down to 1 mm/s), and operating under low illumination exacerbates the problem. Addressing slow relative motions is thus crucial to raise the practicality of EBSPE. To this end, we propose using recurrent vision transformers (RVT) for EBSPE. The key idea is to exploit recurrent memory to capture longer-term information across sparse event streams, and to learn the temporal attention span from full trajectories rather than fixing the time window. We developed an RVT-based heatmap regressor to produce 2D-3D correspondences for pose estimation, which was validated on real event data collected under slow relative motions representative of RPO trajectories. Results indicate the superior pose estimation accuracy and stability of our RVT-based method, relative to previous per-event-frame-based or fixed-time-window EBSPE methods.