Effective Application of Sonar Data for SLAM

다국어 초록 (Multilingual Abstract)

This paper shows how effectively sonar data can be worked with approaches suggested for the indoor SLAM (Simultaneous Localization And Mapping). A sonar sensor occasionally provides wrong distance range due to the wide beam width and the specular reflection phenomenon. To overcome weak points enough to use for the SLAM, several approaches are proposed. First, distance ranges acquired from the same object have been stored by using the FPA (Footprint-association) model, which associates two sonar footprints into a hypothesized circle frame. Using the Least Squares method, a line feature is extracted from the data stored through the FPA model. However phantom features are sometimes generated due to the uncertainties of a sonar sensor. The feature evaluation model is applied to reduce phantom features, which associates the extracted feature with the weighted average probability of grids that are located within the area under the feature uncertainty. By using raw sonar data together with the extracted features as observations, the visibility for landmarks can be improved, and the SLAM performance can be stabilized. Additionally, the SP (Symmetries and Perturbations) model, a representation of uncertain geometric information that combines the probability theory and the theory of symmetries, is applied in this paper. The proposed methods have been tested in a real home environment with a mobile robot.