Building Polygonal Maps from Laser Range Data

This paper presents a new approach to the problem of building a global map from laser range data, utilizing shape based object recognition techniques originally developed for tasks in computer vision. In contrast to classical approaches, the perceived environment is represented by polygonal curves (polylines), possibly containing rich shape information yet consisting of a relatively small number of vertices. The main task, besides segmentation of the raw scan point data into polylines and denoising, is to find corresponding environmental features in consecutive scans to merge the polylinedata to a global map. The correspondence problem is solved using shape similarity between the polylines. The approach does not require any odometry data and is robust to discontinuities in robot position, e.g., when the robot slips. Since higher order objects in the form of polylines and their shape similarity are present in our approach, it provides a link between the necessary low-level and the desired high-level information in robot navigation. The presented integration of spatial arrangement information, illustrates the fact that high level spatial information can be easily integrated in our framework.