Experiments in robust bistatic sonar object classification for local environment mapping

Presents the classification results of a bistatic sonar sensor with decision tree classifier for use in mobile robot navigation. Unlike previous work the paper investigates the discrimination and robustness of the sensor´s classifications when presented with common office objects with complex geometries. The feature extraction process uses a novel perturbed L₂ method which allows a physical interpretation of the features. The robustness of the classifications indicate that, given a suitably enlarged set of training objects, the approach would be suitable for use within office environments.