Yinchuan Wang (王银川)

LiDAR-based SLAM is recognized as one effective method to offer localization guidance in rough environments. However, off-the-shelf LiDAR-based SLAM methods suffer from significant pose estimation drifts, particularly components relevant to the vertical direction, when passing to uneven terrains. This deficiency typically leads to a conspicuously distorted global map. In this article, a LiDAR-based SLAM method is presented to improve the accuracy of pose estimations for ground vehicles in rough terrains, which is termed Rotation-Optimized LiDAR-Only (ROLO) SLAM. The method exploits a forward location prediction to coarsely eliminate the location difference of consecutive scans, thereby enabling separate and accurate determination of the location and orientation at the front-end. Furthermore, we adopt a parallel-capable spatial voxelization for correspondence-matching. We develop a spherical alignment-guided rotation registration within each voxel to estimate the rotation of vehicle. By incorporating geometric alignment, we introduce the motion constraint into the optimization formulation to enhance the rapid and effective estimation of LiDAR’s translation. Subsequently, we extract several keyframes to construct the submap and exploit an alignment from the current scan to the submap for precise pose estimation. Meanwhile, a global-scale factor graph is established to aid in the reduction of cumulative errors. In various scenes, diverse experiments have been conducted to evaluate our method. The results demonstrate that ROLO-SLAM excels in pose estimation of ground vehicles and outperforms existing state-of-the-art LiDAR SLAM frameworks.

Traversability analysis is of vital importance for autonomous vehicle navigation in outdoor environments. This paper focuses on the navigation algorithms in outdoor environments with water hazards, where the water areas can neither simply be neglected nor be deemed as non-traversable for safe and efficient navigation. To this end, we present a tightly-integrated navigation algorithms framework, which includes an environmental traversability analysis model and a control signal acquisition model. The traversability analysis model includes the SwinT-CGRU semantic segmentation model and the ConvCRFs water analysis model proposed in this paper. SwinT-CGRU can efficiently extract the feature from the input successive images, providing initial analysis of environmental traversability. To further analyze the water region, we employ a ConvCRFs, which determines the water traversability in an effective way. The results of the water traversability analysis are convincing for reliable vehicle navigation in outdoor scenarios. Based on the traversability analysis results, we propose a path planning approach by adapting the artificial potential field method to the image space. The control signal for steering and driving the vehicle can be acquired through an established mapping from the image space to the vehicle space. Experiments demonstrate that our segmentation model improves mIoU by 2.09-18.21% on the RELLIS-3D dataset. Furthermore, our navigation algorithm exhibits real-time performance and stability, ensuring the safety of vehicles in outdoor environments.

It is challenging for the mobile robot to achieve autonomous and mapless navigation in the unknown environment with uneven terrain. In this study, we present a layered and systematic pipeline. At the local level, we maintain a tree structure that is dynamically extended with the navigation. This structure unifies the planning with the terrain identification. Besides, it contributes to explicitly identifying the hazardous areas on uneven terrain. In particular, certain nodes of the tree are consistently kept to form a sparse graph at the global level, which records the history of the exploration. A series of subgoals that can be obtained in the tree and the graph are utilized for leading the navigation. To determine a subgoal, we develop an evaluation method whose input elements can be efficiently obtained on the layered structure. We conduct both simulation and real-world experiments to evaluate the developed method and its key modules. The experimental results demonstrate the effectiveness and efficiency of our method. The robot can travel through the unknown uneven region safely and reach the target rapidly without a preconstructed map.