Pose Estimation Beyond Deep Learning

Underwater robots have no reliable GPS, and visual localization breaks down in turbid, low-light water. Sonar is the natural fallback, though acoustic sensing brings its own difficulties, from low signal-to-noise to elevation ambiguity, that make good measurement models hard to build. Purely data-driven pose regression is one route, but it depends on large labeled datasets that are scarce underwater.

This project develops probabilistic pose estimation from sonar: Bayesian state estimation on the pose manifold, with uncertainty propagated coherently from sensing through to decision.

It will deliver methods for sonar-based SLAM and localization and field-validated systems that run them. Benchmarking in the wild requires ground truth that turned out not to exist, so the project also builds the algorithms and hardware rigs to capture it, along with the datasets they produce.