This paper proposes a bandwidth tunable technique for real-time probabilistic scene modeling and mapping to enable co-robotic exploration in communication constrained environments such as the deep sea. The parameters of the system enable the user to characterize the scene complexity represented by the map, which in turn determines the bandwidth requirements. The approach is demonstrated using an underwater robot that learns an unsupervised scene model of the environment and then uses this scene model to communicate the spatial distribution of various high-level semantic scene constructs to a human operator. Preliminary experiments in an artificially constructed tank environment as well as simulated missions over a 10m×10m coral reef using real data show the tunability of the maps to different bandwidth constraints and science interests. To our knowledge this is the first paper to quantity how the free parameters of the unsupervised scene model impact both the scientific utility of and bandwidth required to communicate the resulting scene model.

This paper addresses the problem of ice-relative navigation of underwater robotic vehicles for oceanographic missions under moving sea ice. We define the ice-relative navigation problem and report a novel underwater robot vehicle, Nereid-UI, and its navigation system designed for under-ice oceanographic operations in ice-covered seas. The goal of Nereid-UI is to enable deployments in polar ocean regions traditionally considered difficult or impossible to access such as the ice-ocean interface in marginal ice zones, the water column of ice-covered seas, and the water column underlying ice shelves. This paper also reports the navigation results of Nereid-UI's first under-ice sea trials and science operations conducted under moving multi-year sea ice off the ice breaker Polarstern in the Arctic Ocean at 83° N 6° W - about 200 km NE of Greenland.