Sonar Feature Map Building for a Mobile Robot

Abstract

This paper presents an approach for sonar feature map building. The approach is composed of extracting features at the data-level fusion stage and fusing the extracted features with the registered features in the map at the feature-level fusion stage. A data-level fusion model, termed three measurements association model (TMAM), has been developed for associating three measurements with a line or a point feature. By use of TMAM, different sets of measurements obtained from a single sonar sensor at consecutive steps are associated with the line and point features. Subsequently, the parameters of the identified features are estimated by use of the iterated least square estimation method. Finally, when a feature is extracted, a simple feature-level fusion strategy is used to update the map. The proposed approach has been tested both in simulation and on real data.