Lifelong Mapping using Adaptive Local Maps
Nandan Banerjee, Dimitri Lisin, Jimmy Briggs, Martin Llofriu, and Mario Munich
iRobot Corporation, United States
Occupancy mapping enables a mobile robot to make intelligent planning decisions to accomplish its tasks. Adaptive local maps is an algorithm which represents the occupancy information as a set of overlapping local maps anchored to poses in the robot's trajectory. At any time, a global occupancy map can be rendered from the local maps to be used for path planning. The advantage of this approach is that the occupancy information stays consistent despite the changes in the pose estimates resulting from loop closures and localization updates. The disadvantage, however, is that the number of local maps grows over time. For long robot runs, or for multiple runs in the same space, this growth will result in redundant occupancy information, which will in turn increase the time it takes to render the global map, as well as the memory footprint of the system. In this paper, we propose a novel approach for the maintenance of an adaptive local maps system, which intelligently eliminates redundant local maps, ensuring the robustness and stability required for lifelong mapping.