RingNav: Task-Shaped Circular Memory for Visual Heading

What happened

arXiv:2610.19044v1 Announce Type: new Abstract: Tiny robots have limited computation and memory, yet visual navigation systems often maintain more state than a task requires. RingNav runs at $27.3$Hz on Raspberry Pi Zero 2W and can be deployed on an ultra-low power GAP8 processor at $33.4$Hz.

RingNav learns only the turn between consecutive images, while heading is composed directly on the circle. On the TartanAir Visual SLAM Challenge, RingNav reduces mean absolute turn error by \(27\%\) and accumulated heading error over $4$ sec by \(15\%\) relative to a similarly sized learned-memory model.

The structured update also gives exactly zero turn for identical frames and exact cancellation under sequence reversal. Without fine-tuning (further training of an existing model for a narrower job), RingNav transfers to real-world drone experiments and continues estimating heading under severe visual degradation.