Stefanini, E.; Ciancolini, E.; Settimi, A.; Pallottino, L. Safe and Robust Map Updating for Long-Term Operations in Dynamic Environments. Sensors2023, 23, 6066.
Stefanini, E.; Ciancolini, E.; Settimi, A.; Pallottino, L. Safe and Robust Map Updating for Long-Term Operations in Dynamic Environments. Sensors 2023, 23, 6066.
Stefanini, E.; Ciancolini, E.; Settimi, A.; Pallottino, L. Safe and Robust Map Updating for Long-Term Operations in Dynamic Environments. Sensors2023, 23, 6066.
Stefanini, E.; Ciancolini, E.; Settimi, A.; Pallottino, L. Safe and Robust Map Updating for Long-Term Operations in Dynamic Environments. Sensors 2023, 23, 6066.
Abstract
Ensuring safe and continuous autonomous navigation in long-term mobile robot applications is still challenging. To ensure a reliable representation of the current environment without the need for periodic re-mapping, updating the map is recommended. However, in case of incorrect estimation of robot pose, updating the map can lead to errors that prevent the robot localisation and jeopardize map accuracy. In this paper, we propose a safe LIDAR-based occupancy grid map updating algorithm for dynamic environments taking into account the uncertainties in the estimation of the robot’s pose. The proposed approach allows robust long-term operations as it can recover the robot’s pose, even when it gets lost, to continue the map update process providing a coherent map. Moreover, the approach is robust also to temporary changes in the map due to the presence of dynamic obstacles such as humans and other robots. Results highlighting map quality, localisation performance, and pose recovery, both in simulation and experiments, are reported.
Keywords
Mapping; localisation; Dynamic environments
Subject
Engineering, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
