AI & ML
Spacecraft Pose Tracking via Kinematic Prediction and Selective Event Correction
arXiv cs.RO0 views
arXiv:2610.19465v1 Announce Type: new Abstract: In initialized spacecraft tracking, a short-horizon motion prediction remains available as event support changes. Observation-Aligned Event Innovation (OA-EI) uses events to correct selected components of this prediction. A constant-body-twist model propagates the complete pose; the event encoder proposes rotational and normalized image-plane translation corrections, retaining propagated axial depth. Candidate magnitudes come from a bounded Scale Gate and a deterministic activity factor, with empty windows returning the prediction unchanged. Evaluation uses four FRESH physical-event trajectories, twenty rollout starts, and three training seeds. Each rollout starts from two reference poses and then uses self-estimates. At 7.7 seconds, rotation and translation errors fall from 41.63{\deg}/57.28 mm for prediction alone to 15.73{\deg}/22.41 mm for OA-EI, reductions of 62.2% and 60.9%. Failure rates fall from 20.0% to 1.67%. Matched ablations favor the rotation-bearing interface for overall pose accuracy; combining scaling with activity support gives the lowest errors at every tested event-retention level. On Jetson Orin Nano, the measured in-memory update takes 44.56 milliseconds at the median and 58.77 milliseconds at P95.
Source:https://arxiv.org/abs/2610.19465PreviousParaphrase Consensus Is Not Self-Consistency: Instruction Reliance and Robustness in VLA PoliciesNextActive Generative Simulation: Meta-Learning What to Simulate for Sim-and-Real Policy Co-Training
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