Efficient 2D LIDAR-Based Map Updating For Long-Term Operations in Dynamic Environments

Abstract

Long-time operations of autonomous vehicles and mobile robots in logistics and service applications are still a challenge. To avoid a continuous re-mapping, the map can be updated to obtain a consistent representation of the current environment. In this paper, we propose a novel LIDARbased occupancy grid map updating algorithm for dynamic environments. The proposed approach allows robust long-term operations as it can detect changes in the working area even in presence of moving elements. Results highlighting map quality and localisation performance, both in simulation and experiments, are reported.

Safe and Robust Map Updating for Long-Term Operations in Dynamic Environments

“…Although the proposed map updating method [7] provides promising results in map quality and localisation performance in a dynamic environment, the method relies heavily on accurate knowledge about the initial pose of the robot in a previously built map. That information might not be available with high accuracy, and due to changes in the map, it might be hard to estimate immediately.…”

“…To evaluate the quality of our updated map with respect to the ground truth ones, we adopt, as in [7], three metrics, reported here for reader convenience. All the ground truth maps were built using the ROS Slam Toolbox package [32].…”

“…To fully understand the necessity of both map update interruption and localisation check introduction, we provide the comparison results with our old approach [7] in Appendix A.3.…”

“…The tolerance tol(z i ) is a function of the measured range and is determined by the parameter Dth in Equation ( 4). To fully understand the necessity of such an interruption in the map-updating process, we provide the comparison results of our old approach [7] (where the map was always updated) with the one proposed in this paper with an initial localisation error. The comparison is performed in the scenario reported in Figure A4: the first environment W 1 is in Figure A4a, while the robot is operating in world W 2 (Figure A4b).…”

“…In our previous paper [7], a 2D Lidar-based map-updating method to detect possible changes over time in the environment and to accordingly update a previously built map with limited memory usage while neglecting the presence of highly dynamic obstacles was proposed. The system was developed using the Robot Operating System framework [8] using a 2D occupancy grid map representation.…”