SkiMap: An Efficient Mapping Framework for Robot Navigation
This addresses mapping efficiency problems for robotics applications, though it appears to be an incremental improvement over existing data structures.
The authors tackled the problem of inefficient mapping for robot navigation by developing SkiMap, a framework that uses a Tree of SkipLists data structure to efficiently generate multiple map representations. Their approach demonstrated better time efficiency than Octrees while maintaining similar memory usage for large workspaces.
We present a novel mapping framework for robot navigation which features a multi-level querying system capable to obtain rapidly representations as diverse as a 3D voxel grid, a 2.5D height map and a 2D occupancy grid. These are inherently embedded into a memory and time efficient core data structure organized as a Tree of SkipLists. Compared to the well-known Octree representation, our approach exhibits a better time efficiency, thanks to its simple and highly parallelizable computational structure, and a similar memory footprint when mapping large workspaces. Peculiarly within the realm of mapping for robot navigation, our framework supports realtime erosion and re-integration of measurements upon reception of optimized poses from the sensor tracker, so as to improve continuously the accuracy of the map.