Fusion of triangulated sonar plus infrared sensing for localization and mapping

We present a novel approach that incorporates information from sonar and infrared sensors mounted on a rotating platform to obtain feature-based stochastic maps of the environment. The purpose is to reliably determine the position of a robot and the features in its environment using low cost sensors. Line and corner features are extracted from the sonar sensors by means of triangulation from multiple vantage points, while line features are extracted from the infrared sensors in separate processes. RANSAC-based approaches are used to extract the features from sonar data and from infrared data. An extended Kalman filter is used to update the position of the robot and the features. The addition of infrared data to sonar data provides more accurate and compact maps.