Abstract
Accurate and reliable state estimation and mapping are the foundation of most autonomous driving systems. In recent years, researchers have focused on pose estimation through geometric feature matching. However, most of the works in the literature assume a static scenario. Moreover, a registration based on a geometric feature is vulnerable to the interference of a dynamic object, resulting in a decline of accuracy. With the development of a deep semantic segmentation network, we can conveniently obtain the semantic information from the point cloud in addition to geometric information. Semantic features can be used as an accessory to geometric features that can improve the performance of odometry and loop closure detection. In a more realistic environment, semantic information can filter out dynamic objects in the data, such as pedestrians and vehicles, which lead to information redundancy in generated map and map-based localization failure. In this paper, we propose a method called LiDAR inertial odometry (LIO) with loop closure combined with semantic information (LIO-CSI), which integrates semantic information to facilitate the front-end process as well as loop closure detection. First, we made a local optimization on the semantic labels provided by the Sparse Point-Voxel Neural Architecture Search (SPVNAS) network. The optimized semantic information is combined into the front-end process of tightly-coupled light detection and ranging (LiDAR) inertial odometry via smoothing and mapping (LIO-SAM), which allows us to filter dynamic objects and improve the accuracy of the point cloud registration. Then, we proposed a semantic assisted scan-context method to improve the accuracy and robustness of loop closure detection. The experiments were conducted on an extensively used dataset KITTI and a self-collected dataset on the Jilin University (JLU) campus. The experimental results demonstrate that our method is better than the purely geometric method, especially in dynamic scenarios, and it has a good generalization ability.
Highlights
State estimation and mapping are fundamental parts of an autonomous driving system, which are the kernel idea of simultaneous localization and mapping (SLAM)
To address the above issues, we propose a method called lidar inertial odometry with loop closure combined with semantic information (LIO-CSI)
We compared the proposed LIO-CSI method with two purely geometric information methods based on tightly-coupled light detection and ranging (LiDAR) inertial odometry, LeGOLOAM and the baseline method LIO-SAM
Summary
State estimation and mapping are fundamental parts of an autonomous driving system, which are the kernel idea of simultaneous localization and mapping (SLAM). The state-of-the-art methods use the geometric feature matches of previous and current frames to estimate the pose (e.g., LiDAR odometry and mapping (LOAM) [1], LOAM-Livox [2], and LeGO-LOAM [3]). Such approaches generally assume that the scenarios are static without great change, and most extracted features of a point cloud are fixed in space. The objects in dynamic scenarios cause the failure of place recognition because these dynamic objects may not be in their original positions when the vehicle returns to the same place This brings challenges for loop closure detection and map-based relocation
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