ColMap: A memory-efficient occupancy grid mapping framework

Abstract In order to possess a significant degree of autonomy, a robot must be able to perceive its environment and store a representation of that environment for use in tasks such as localization, navigation, collision avoidance, and higher decision making. It must do this subject to constraints on memory and processing power typical of the embedded computer systems commonly found on small robotic devices. These constraints are particularly important for flying robots (i.e. unmanned aerial vehicles), for which weight must be minimized. The challenge of storing a detailed map of a large area on a small embedded computer has led to the development of many algorithms that exploit the sparsity of typical maps to create a more memory-efficient representation. In this paper, we demonstrate that the verticality of both natural and man-made structures can be exploited to create a framework that can store occupancy grid maps efficiently, without causing additional computational burden. The new framework achieves an order-of-magnitude reduction in memory footprint relative to widely-used occupancy grid mapping software, while also achieving a slight speed-up in map insertion and access times. We also make available LIDAR scans taken from a hexacopter of an indoor flight arena that can be used to assist in evaluating future mapping and SLAM developments.