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 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 identical frames and exact cancellation under sequence reversal.

Without fine-tuning, RingNav transfers to real-world drone experiments and continues estimating heading under severe visual degradation. 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.