Improving the Robustness of Weighted Scan Matching with Quad Trees

This paper presents the improvement the robustness and accuracy of the weighted scan matching algorithm matching against the union of earlier acquired scans. The approach allows to reduce the correspondence error, which is explicitly modeled in the weighted scan matching algorithm, by providing a more complete and denser frame of reference to match new scans. By making use of the efficient quad tree data structure, earlier acquired scans can be stored with millimeter accuracy for environments with dimensions larger than 100x100 meter. This can be realized with the preservation of real-time performance. In our experiments we illustrate the significant gains in robustness and accuracy that can be the result with this approach.