  The autonomous mapping of large-scale urban scenes presents significant
challenges for autonomous robots. To mitigate the challenges, global planning,
such as utilizing prior GPS trajectories from OpenStreetMap (OSM), is often
used to guide the autonomous navigation of robots for mapping. However, due to
factors like complex terrain, unexpected body movement, and sensor noise, the
uncertainty of the robot's pose estimates inevitably increases over time,
ultimately leading to the failure of robotic mapping. To address this issue, we
propose a novel active loop closure procedure, enabling the robot to actively
re-plan the previously planned GPS trajectory. The method can guide the robot
to re-visit the previous places where the loop-closure detection can be
performed to trigger the back-end optimization, effectively reducing errors and
uncertainties in pose estimation. The proposed active loop closure mechanism is
implemented and embedded into a real-time OSM-guided robot mapping framework.
Empirical results on several large-scale outdoor scenarios demonstrate its
effectiveness and promising performance.
