TerrLab - a generic simulation and post-processing tool for terrain referenced navigation

One of the challenges in underwater navigation for autonomous vehicles is to get position updates for the navigation system. For deep water applications or operations requiring a high degree of covertness, surfacing for GPS is not a solution. The conventional submerged position update techniques, like DGPS-USBL or LBL, requires external physical infrastructure in the operation area of the vehicle. Terrain referenced navigation is a promising technique for submerged position updates. It requires only a bathymetric map of the operation area and the use of a bathymetric sensor during the operation. Different algorithms for terrain referenced navigation have already successfully been tested for underwater applications. These algorithms include variants of the original TERCOM, different Kalman filter based techniques and non-linear Bayesian estimators like the point mass filter and particle filters. To asses the performance of the different algorithms and their robustness on both real and simulated data, FFI has developed a tool called TerrLab. The tool was originally used to qualify algorithms for the real-time terrain navigation system for the HUGIN AUVs. It is now also used to test different sensor and DTM error models for the algorithms.