Feature-Based Mapping in Real, Large Scale Environments Using an Ultrasonic Array

This paper presents a strategy for achieving practical mapping navigation using a wheeled mobile robot equipped with an advanced sonar sensor The original mapping navigation experiment, carried out with the same robot configuration, built a feature map consisting of common-place indoor landmarks crucial for localization; namely, planes, corners, and edges. The map exhaustively maintained covariance matrices among all features, and thus presented a time and memory impediment to practical navigation in large environments. The new local mapping strategy proposed here breaks down a large environment into a topology of local regions, only maintaining the covariance among features in the same local region, and the covariance among local maps. This notion of two-hierarchy representation drastically improves the memory and processing-time requirements of the original global approach, while preserving the statistical details, in the authors' opinions, necessary for an accurate map and prolonged navigation. The new local mapping scheme also extends the endeavor toward reducing error accumulation made in the global mapping strategy by eliminating errors accumulated between visits to the same part of the environment. This is achieved with a map-matching strategy developed exclusively for the advanced sonar sensor that is employed. The local mapping strategy has been tested in two large, real-life indoor environments, and successful results are reported here.