Loop closure detection has the potential to correct the drift of trajectories and build a global consistent map in LiDAR SLAM, however it remains a challenging problem in outdoor environment due to the sparsity of 3D point clouds data, large-scale scenes and moving objects. Inspired by the way humans perceive the environment through recognizing objects and identifying their relations, this paper presents a novel descriptor that contains semantic and topological information for loop closure detection. Unlike most existing methods that extract features from the raw point clouds or use all semantic objects, we directly discard point clouds representing pedestrians and vehicles after semantic segmentation. Then, we propose a semantic topological graph representation from the remaining point clouds and convert this graph into a descriptor. Additionally, we propose a two-stage algorithm for matching descriptors to efficiently determine the loop. Our method has been extensively evaluated using the KITTI dataset and outperforms state-of-the-art methods, especially in the challenging situations such as viewpoint changes and dynamic scenes.
|Title of host publication||2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)|
|Number of pages||8|
|Publication status||Published online - 26 Dec 2022|
|Event||IEEE/RSJ International Conference on Intelligent Robots and Systems - Kyoto, Japan|
Duration: 23 Oct 2022 → 27 Oct 2022
|Name||IEEE International Conference on Intelligent Robots and Systems|
|Conference||IEEE/RSJ International Conference on Intelligent Robots and Systems|
|Abbreviated title||IROS 2022|
|Period||23/10/22 → 27/10/22|
Bibliographical noteThis work was supported by Natural Science Foundation of China (No. 61973066, 61471110) , Major Science and Technology Projects of Liaoning Province (No. 2021JH1/10400049), Foundation of Key Laboratory of Aerospace System Simulation (No. 6142002200301), Foundation of Key Laboratory of Equipment Reliability (No. WD2C20205500306), Open Research Projects of Zhejiang Lab (No. 2019KD0AD01/006) and Major Science and Technology Innovation Engineering Projects of Shandong Province (No.2019JZZY010128).