A novel mmWave radar SLAM method is proposed to integrate multi-dimensional information, including velocity, spatial, RCS, and semantics to enable autonomous navigation in subterranean tunnel environments, which are characterized by low visibility and degraded conditions. By combining doppler odometry, scan matching, and feature matching modules, the proposed method effectively mitigates environmental degradation. Experimental results from various subterranean tunnel trajectories show that the proposed method achieves superior localization and mapping accuracy compared to existing radar SLAM methods, and even outperforms state-of-the-art Lidar SLAM methods. The achieved accuracy can reach the decimeter level in numerous subterranean tunnel tracks covering a total distance of over 500 meters.
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Zeng et al. (2024) studied this question.
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