P2-LOAM: LiDAR Odometry and Mapping with Pole-Plane Landmark
For spatial perception, object-level SLAM (Simultaneous Localization and Mapping) has shown an advantage by leveraging semantic information to comprehend unknown environments. Poles are considered significant semantic landmark objects in urban roads and man-made constructions like stone pillars, str...
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| Vydáno v: | IEEE Conference on Industrial Electronics and Applications (Online) s. 1 - 7 |
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| Jazyk: | angličtina |
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IEEE
05.08.2024
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| ISSN: | 2158-2297 |
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| Abstract | For spatial perception, object-level SLAM (Simultaneous Localization and Mapping) has shown an advantage by leveraging semantic information to comprehend unknown environments. Poles are considered significant semantic landmark objects in urban roads and man-made constructions like stone pillars, street light lamps, and tree trunks. The rich pole land-marks enhance to the robustness and accuracy of SLAM. In this paper, we propose a LiDAR-based object-level SLAM named p 2 - LOAM, which simultaneously estimates the pose and constructs a sparse pole landmark map. We propose a multi-RANSAC method for pole segmentation and estimate the parametric representation of pole objects in various scenes. Based on the segmented pole outcome, a coarse-to-fine data association for the pole object method is designed. Furthermore, a plane-assisted cost function for the pole landmark residual construction is developed. We demonstrate the accuracy and robustness of the proposed method in public datasets and real-world experiments. |
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| AbstractList | For spatial perception, object-level SLAM (Simultaneous Localization and Mapping) has shown an advantage by leveraging semantic information to comprehend unknown environments. Poles are considered significant semantic landmark objects in urban roads and man-made constructions like stone pillars, street light lamps, and tree trunks. The rich pole land-marks enhance to the robustness and accuracy of SLAM. In this paper, we propose a LiDAR-based object-level SLAM named p 2 - LOAM, which simultaneously estimates the pose and constructs a sparse pole landmark map. We propose a multi-RANSAC method for pole segmentation and estimate the parametric representation of pole objects in various scenes. Based on the segmented pole outcome, a coarse-to-fine data association for the pole object method is designed. Furthermore, a plane-assisted cost function for the pole landmark residual construction is developed. We demonstrate the accuracy and robustness of the proposed method in public datasets and real-world experiments. |
| Author | Guan, Yisheng Mao, Shixin Xu, Jianhong Chen, Weinan Zhu, Haifei |
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| SubjectTerms | Accuracy Design methodology Industrial electronics Laser radar Location awareness pole landmark Roads Simultaneous localization and mapping SLAM |
| Title | P2-LOAM: LiDAR Odometry and Mapping with Pole-Plane Landmark |
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