Depth image matching for underwater vehicle navigation

This paper presents a map-based navigation for autonomous underwater vehicles (AUV) that aims to enhance the localisation without external aid. Bathymetric profiles given by a multibeam echosounder are matched to a reference depth map (RDM). The RDM is an existing chart or is constructed from an AUV survey at high altitude. The echosounder data obtained close to the bottom are integrated in a local depth map (LDM). The matching algorithm uses contour-like points extracted after image smoothing. Differential attributes allow us to identify possible rough matches that initialise the iterative position and orientation estimator. The paper presents new results of the study started in 1994. Major improvements concern image smoothing, estimator initialisation, and in-line evaluation of statistical parameters, making completely automated position estimation possible. First results on real bathymetric data are presented