Online and consistent occupancy grid mapping for planning in unknown environments

Abstract

Actively exploring and mapping an unknown environment requires integration of both simultaneous localization and mapping (SLAM) and path planning methods. Path planning relies on a map that contains free and occupied space information and is efficient to query, while the role of SLAM is to keep the map consistent as new measurements are
continuously added. A key challenge, however, lies in ensuring a map representation compatible with both these objectives: that is, a map that maintains free space information for planning but can also adapt efficiently to dynamically changing pose
estimates from a graph-based SLAM system.

In this paper, we propose an online global occupancy map that can be corrected for accumulated drift efficiently based on incremental solutions from a sparse graph-based SLAM optimization. Our map maintains free space information for real-time path planning while undergoing a bounded number of updates in each loop closure iteration. We evaluate performance for both simulated and real-world datasets for an application
involving underwater exploration and mapping.