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We present results of the application of a simultaneous localisation and map building (SLAM) algorithm to estimate the motion of a submersible vehicle. Scans obtained from an on-board sonar are processed to extract stable point features in the environment. These point features are then used to build up a map of the environment while simultaneously providing estimates of the vehicle location. Results are shown from deployment in a swimming pool at the University of Sydney as well as from field trials in a natural environment along Sydney's coast. This work represents the first instance of a deployable underwater implementation of the SLAM algorithm.
Williams et al. (Thu,) studied this question.