RingNav: Task-Shaped Circular Memory for Visual Heading
Tiny robots have limited computation and memory, yet visual navigation systems often maintain more state than a task requires. We ask whether persistent memory can instead be shaped by the task. We present \textbf{RingNav}, a \(10.6\)k--parameter visual heading estimator that constrains its memory to the circular manifold \(S^1\). 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 id...
| Authors | Naitri Rajyaguru, Aryan Tajne, Stephen Jardim et al. |
|---|---|
| Primary category | cs.RO |
| Published | 13 October 2026 |
| Last updated at source | 13 October 2026 |
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