Feature-based map building using sparse sonar data in a home-like environment

Abstract

This study developed and implemented a new feature-based map-building model that uses only sparsely sampled sonar data from a fixed ring with 16 sonar sensors. It introduces two kinds of data filter approaches to overcome challenges associated with sonar sensors, such as a wide beam aperture and the specular reflection effect. The first approach is a footprint-association (FPA) model, which associates two sonar footprints into a hypothesized circle frame in order to determine the feature type, such as a line, a point, or an arc. The FPA model provides information about the trace of centers of hypothesized circles. It extracts features from a cluster composed of more than two independent footprints that originate from the same object. The other approach is a feature-association (FTA) model, which associates a new sonar footprint into extracted features to update the feature. Both proposed methods were tested in a home-like environment using a mobile robot.