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An accurate description of laneway space with self-localization is a key issue when coal mine rescue robots (CMRRs) perform post-disaster exploration and rescue missions. The 3D simultaneous localization and mapping (SLAM) is an effective but time-critical and highly challenging task in complex laneway scenarios, especially after disasters. In this paper, we propose a novel real-time 3D SLAM based on normally distributed transform (NDT) that employs pose graph optimization and loop closure to further improve mapping consistency. We innovatively extract floors and walls in the laneway as plane nodes to construct landmark constraints, in addition to applying pose nodes from the lidar odometry via NDT. A lightweight and effective loop detection method is conducted using odometry with an appearance-based approach to building a globally consistent map. The proposed method was evaluated on a public dataset, and field tests in an underground coal mine were performed. Results indicate that our algorithm can achieve lower computational complexity and drift, which can provide pose estimation and environment description for CMRRs to realize remote control assistance and automatic navigation in coal mine rescue missions.

Efficient Laser-Based 3D SLAM for Coal Mine Rescue Robots