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.

翻译：大规模城市场景的自主建图对自主机器人提出了重大挑战。为应对这些挑战，通常采用全局规划（例如利用OpenStreetMap（OSM）提供的先验GPS轨迹）来引导机器人进行自主导航建图。然而，由于复杂地形、意外机体运动及传感器噪声等因素，机器人位姿估计的不确定性会随时间推移不可避免地增加，最终导致建图失败。针对该问题，本文提出一种新颖的主动闭环流程，使机器人能够主动重新规划先前设定的GPS轨迹。该方法可引导机器人主动重访能够执行闭环检测的历史位置，从而触发后端优化，有效降低位姿估计的误差与不确定性。所提出的主动闭环机制已实现并嵌入实时OSM引导的机器人建图框架中。在多个大规模户外场景中的实证结果验证了该方法的有效性与优越性能。