Feature extraction and data association for AUV concurrent mapping and localisation

This paper describes a Concurrent Mapping and Localisation (CML) algorithm suitable for localising an Autonomous Underwater Vehicle (AUV). The proposed CML algorithm uses a standard off-the-shelf sonar for sensing the environment. The returns from the sonar are used to detect targets in the vehicle's vicinity. These targets are used in conjunction with a vehicle model by the CML algorithm to concurrently build an absolute map of the environment and localise the vehicle in absolute coordinates. In order for the algorithm to work, the stored targets must be associated to the sonar returns at each iteration. Given the nature of sonar data, false returns complicate this process. The choice of targets and a suitable data association strategy is, therefore, vital. The chosen targets consist of returns of a significant strength. The segmentation detects these targets and calculates (a) the relative position of their center of mass with respect to the vehicle, (b) the targets' surface size, and (c) the targets' first invariant moment. This information is used by the system to perform the data association. We have chosen to adapt the well known Multiple Hypothesis Tracking Filter (MHTF) [1] to the CML structure. This is a measurement-oriented approach that finds the probability that an established target gave rise to a certain return. The paper presents results with real sonar data.