{"type":"ScholarlyArticle","title":"A New Guaranteed Outlier Removal Method Based on Plane Constraints for Large-Scale LiDAR Point Cloud Registration","authors":[{"name":"Gang Ma","slug":"gang-ma","given":"Gang","family":"Ma","affiliation":"School of Computer Science , Fudan University , China"},{"name":"Hui Wei","slug":"hui-wei","given":"Hui","family":"Wei","affiliation":"School of Computer Science , Fudan University , China"},{"name":"Runfeng Lin","slug":"runfeng-lin","given":"Runfeng","family":"Lin","affiliation":"School of Computer Science , Fudan University , China"},{"name":"Jialiang Wu","slug":"jialiang-wu","given":"Jialiang","family":"Wu","affiliation":"School of Computer Science , Fudan University , China"}],"abstract":"In this paper, we present a novel registration method based on plane constraints for large-scale LiDAR point clouds, effectively decoupling rotation estimation and translation estimation. For rotation estimation, we propose an outlier removal method that combines coarse filtering with rotation-invariant constraints and refined filtering based on computational geometric consistency checks, effectively pruning outliers and robustly estimating accurate relative rotations from plane normals. In translation estimation, we propose a component-wise method based on plane translation constraints to efficiently estimate relative translations. The robustness and effectiveness of our proposed method are empirically validated on three popular LiDAR point cloud datasets. The experimental results convincingly demonstrate that our approach achieves state-of-the-art performance.","doi":"10.24963/ijcai.2024/759","url":"https://ijcai.org/proceedings/2024/759","pdf":"https://ijcai.org/proceedings/2024/0759.pdf","markdown":"https://ijcai.org/proceedings/2024/759.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/759","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Robotics","track":"Main Track","number":759,"pages":{"first":6868,"last":6876},"keywords":["Computer Vision: CV: Scene analysis and understanding","Robotics: ROB: Robotics and vision","Robotics: ROB: Perception","Computer Vision: CV: 3D computer vision"],"videos":[],"references":[{"position":1,"raw":"[Ao et al., 2021] Sheng Ao, Qingyong Hu, Bo Yang, An- drew Markham, and Yulan Guo. 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