Terrain-aided navigation for long-range AUVs in dynamic under-mapped environments

Deploying long-range autonomous underwater vehicles (AUVs) mid-water column in the deep ocean is one of the most challenging applications for these submersibles. Without external support and speed over the ground measurements, dead-reckoning (DR) navigation inevitably experiences an error proportional to the mission range and the speed of the water currents. In response to this problem, a computationally feasible and low-power terrain-aided navigation (TAN) system is developed. A Rao-Blackwellized Particle Filter robust to estimation divergence is designed to estimate the vehicle's position and the speed of water currents. To evaluate performance, field data from multiday AUV deployments in the Southern Ocean are used. These form a unique test case for assessing the TAN performance under extremely challenging conditions. Despite the use of a small number of low-power sensors and a Doppler velocity log to enable TAN, the algorithm limits the localisation error to within a few hundreds of metres, as opposed to a DR error of 40 km, given a 50 m resolution bathymetric map. To evaluate further the effectiveness of the system under a varying map quality, grids of 100, 200, and 400 m resolution are generated by subsampling the original 50 m resolution map. Despite the high complexity of the navigation problem, the filter exhibits robust and relatively accurate behaviour. Given the current aim of the oceanographic community to develop maps of similar resolution, the results of this study suggest that TAN can enable AUV operations of the order of months using global bathymetric models.