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

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 remapping, updating the map is recommended. However, in the case of incorrect robot pose estimation, updating the map can lead to errors that prevent the robot’s localisation and jeopardise map accuracy. In this paper, we propose a safe Lidar-based occupancy grid map-updating algorithm for dynamic environments, taking into account uncertainties in the estimation of the robot’s pose. The proposed approach allows for 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 also robust 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

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

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 LIDAR-based occupancy grid map updating algorithm for dynamic environments taking into account possible localisation and measurement errors. 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