EchoBelief: Evidence-Anchored Spatial Memory for Active Acoustic Mapping

Echoes encode room geometry, and learned models can infer room layouts from room impulse responses (RIRs). However, one RIR mainly reveals nearby walls in direct view, so a robot mapping a whole room must decide where to move, when to measure and when to stop, as every measurement has a cost. Existing acoustic methods take responses as given, and active systems rely on vision; none chooses measurements from a persistent acoustic map. Such a map faces a dilemma: treating learned predictions as measurements erases observed walls, while discarding them loses spatial context. We propose EchoBelief, a system that resolves this dilemma with evidence-anchored spatial memory: an evidence map stores only structure that echoes or executed motion support, and a separate recurrent memory holds learned predictions. A geometry-supervised polar encoder turns each RIR into a map of wall reflections, and a belief-guided PPO policy reads the memory to move, sense or stop under an acquisition budget. We also introduce EchoRooms, a benchmark of 4,026 simulated rooms across 36 room types and five difficulty tiers, with 1.58M posed 12-channel RIRs on a dense 1 m grid, which we will release. On 355 test rooms, with known poses, EchoBelief reaches 90 % wall recall in 70.1 % of episodes, against 49.4–52.0 % for three tuned planners, while sensing half as often. The gain is largest in large rooms, where the planners rarely succeed (52.9% vs. at most 8.2%); in smaller rooms, the planners match or exceed it but sense far more. On 60 rooms with geometry disjoint from EchoRooms, evaluated only once, EchoBelief still outperforms raycast planning, and retraining without the memory pathway costs 22.8 success points.