Recurrent Vision Transformers for Event-Based Spacecraft Pose Estimation Under Slow Relative Motions

Key points

• 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.1
• To this end, we propose using recurrent vision transformers (RVT) for EBSPE.1
• 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.1
• 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.1

Sources (1)

• [1]Recurrent Vision Transformers for Event-Based Spacecraft Pose Estimation Under Slow Relative Motions
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 12:52 AM

  Vision-based object pose estimation is vital for conducting spacecraft rendezvous and proximity operations (RPO).

  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.

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