Construction of Global Maps with Polygonal Objects from Laser Range Data

L.J. Latecki, R. Lakaemper, X. Sun (USA), and D. Wolter (Germany)


Robot navigation, global map, shape similarity, SLAM, 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 de veloped for tasks in computer vision. In contrast to clas sical approaches, the perceived environment is represented by polygonal curves (polylines), possibly containing rich shape information yet consisting of a relatively small num ber of vertices. The main task, besides the segmentation of raw scan point data into polylines and the removal of noise, is to find corresponding environmental features in consec utive scans to merge the polyline data 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 pose, e.g., when the robot slips. Since higher order objects in the form of polylines are present, our representation is well suited to maintaining global consistency.

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