Multi-scale point and line range data algorithms for mapping and localization

This paper presents a multi-scale point and line based representation of two-dimensional range scan data. The techniques are based on a multi-scale Hough transform and a tree representation of the environment's features. The multi-scale representation can lead to improved robustness and computational efficiencies in basic operations, such as matching and correspondence, that commonly arise in many localization and mapping procedures. For multi-scale matching and correspondence we introduce a chi² criterion that is calculated from the estimated variance in position of each detected line segment or point. This improved correspondence method can be used as the basis for simple scan-matching displacement estimation, as a part of a SLAM implementation, or as the basis for solutions to the kidnapped robot problem. Experimental results (using a Sick LMS-200 range scanner) show the effectiveness of our methods