Efficient Grid Map Data Structures for Autonomous Driving in Large-Scale Environments

In this paper, we investigate data structures for grid mapping in autonomous driving. While the traversed environment can be virtually infinitely large, one is mainly interested in the immediate surroundings of the vehicle. Thus, a world-fixed, square grid map centered roughly at the vehicle is used. For an efficient data handling, this grid map is subdivided into multiple submaps, which can be added and removed in order to move the currently considered window with the vehicle. We evaluate different choices for the underlying data structures (array, hashmap, quadtree) as well as different submap sizes on both a theoretical and practical level: We provide a complexity analysis and a discussion on possible overheads. Furthermore, we present empirical results regarding memory consumption and runtime of common operations (mapping, scan matching, map retrieval) on real-world datasets of different scenarios (sub-urb, rural area, highway). Finally, we give recommendations depending on the actual requirements of the used algorithm.