Robust navigation in unstructured environments with SLAM-assisted NDT and divergence-guided temporal point cloud fusion
Unstructured environments challenge unmanned ground vehicle (UGV) navigation with complex terrain and open-set obstacles. Existing inertial-aided navigation using external odometry suffers from localization errors in rugged off-road conditions, while sparse LiDAR point clouds degrade traversability prediction. The purpose of this study is to address these limitations by developing a robust autonomous navigation framework that integrates SLAM-assisted normal distributions transform (SANDT) and divergence-guided temporal point cloud fusion. First, the authors replace conventional vehicle odometry with inertial data maintained by a LiDAR-based SLAM method, supplying a continuous and stable coarse guess for NDT registration to improve localization. Second, voxel-wise NDT representations of adjacent point clouds are computed; key historical frames are selected via Jensen-Shannon divergence and fused to densify the current point cloud and improve traversability estimation. Finally, the authors integrate these components into an autonomous navigation framework and validate it in real-world scenarios. Experiments demonstrate that the authors’ framework achieves accurate localization and seamless indoor-to-outdoor navigation, outperforming baseline methods in traversability prediction, navigation success rate and obstacle avoidance. This paper presents a robust autonomous navigation framework for unstructured environments. SANDT enables cross-scene navigation in complex terrains, and global divergence-based temporal fusion pioneers LiDAR-based traversability estimation. Further details on localization, traversability prediction and real-world navigation are provided in the supplementary video.