Active Loop Closure for OSM-guided Robotic Mapping in Large-Scale Urban Environments

TL;DR

This paper introduces an active loop closure method that improves large-scale urban robotic mapping by re-planning GPS trajectories to reduce pose estimation errors, demonstrated through real-world outdoor experiments.

Contribution

It presents a novel active loop closure approach integrated into an OSM-guided mapping framework, enhancing pose accuracy in large-scale urban environments.

Findings

Effective reduction of pose estimation errors

Improved mapping accuracy in outdoor scenarios

Real-time implementation demonstrated

Abstract

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.