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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Published in:IEEE Conference on Industrial Electronics and Applications (Online) pp. 1 - 7
Main Authors: Xu, Jianhong, Chen, Weinan, Mao, Shixin, Guan, Yisheng, Zhu, Haifei
Format: Conference Proceeding
Language:English
Published: IEEE 05.08.2024
Subjects:
Accuracy
Design methodology
Industrial electronics
Laser radar
Location awareness
pole landmark
Roads
Simultaneous localization and mapping
SLAM
ISSN:2158-2297
Online Access:Get full text
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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.
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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Snippet For spatial perception, object-level SLAM (Simultaneous Localization and Mapping) has shown an advantage by leveraging semantic information to comprehend...
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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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