Robust weighted scan matching with quadtrees

This paper presents the improvement of 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 quadtree 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

A scalable hybrid multi-robot SLAM method for highly detailed maps

Recent successful SLAM methods employ hybrid map representations combining the strengths of topological maps and occupancy grids. Such representations often facilitate multi-agent mapping. In this paper, a successful SLAM method is presented, which is inspired by the manifold data structure by Howard et al. This method maintains a graph with sensor observations stored in vertices and pose differences including uncertainty information stored in edges. Through its graph structure, updates are local and can be efficiently communicated to peers. The graph links represent known traversable space, and facilitate tasks like path planning. We demonstrate that our SLAM method produces very detailed maps without sacrificing scalability. The presented method was used by the UvA Rescue Virtual Robots team, which won the Best Mapping Award in the RoboCup Rescue Virtual Robots competition in 2006