Feature extraction and scene interpretation for map-based navigation and map building

A scheme for extracting environment features from 1D range data and their interpretation is presented. Segmentation is done by deciding on a measure of model fidelity which is applied to adjacent groups of measurements. The extraction process is considered to include a subsequent matching step where segments which belong to the same landmark ar to be merged while keeping track of those which originate from distinct features. This is done by an agglomerative hierarchical clustering algorithm with a Mahalanobis distance matrix. The method is discussed with straight line segments which are found in a generalized least squares sense using polar coordinates including their first-order covariance estimates. As a consequence, extraction is no longer a real time problem on the level of single range readings, but must be treated on the level of whole scans. Experimental results with three commercially available laser scanners are presented. The implementation on a mobile robot which performs a map-based localization demonstrate the accuracy and applicability of the method under real time conditions. The collection of line segments and associated covariance matrices obtained from the extraction process contains more information about the scene than is required for map-based localization. In a subsequent reasoning step this information is made explicit. By successive abstraction and consequent propagation of uncertainties, a compact scene model is finally obtained in the form of a weighted symbolic description preserving topology information and reflecting the main characteristics of a local observation.