Weighted Conformal LiDAR-Mapping for Structured SLAM
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913223538638848 |
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| author | Prieto-Fernández, Natalia Fernández-Blanco, Sergio Fernández-Blanco, Álvaro Benítez-Andrades, José Alberto Carro-De-Lorenzo, Francisco Benavides, Carmen |
| author_facet | Prieto-Fernández, Natalia Fernández-Blanco, Sergio Fernández-Blanco, Álvaro Benítez-Andrades, José Alberto Carro-De-Lorenzo, Francisco Benavides, Carmen |
| contents | One of the main challenges in simultaneous localization and mapping (SLAM) is real-time processing. High-computational loads linked to data acquisition and processing complicate this task. This article presents an efficient feature extraction approach for mapping structured environments. The proposed methodology, weighted conformal LiDAR-mapping (WCLM), is based on the extraction of polygonal profiles and propagation of uncertainties from raw measurement data. This is achieved using conformal M bius transformation. The algorithm has been validated experimentally using 2-D data obtained from a low-cost Light Detection and Ranging (LiDAR) range finder. The results obtained suggest that computational efficiency is significantly improved with reference to other state-of-the-art SLAM approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_03376 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Weighted Conformal LiDAR-Mapping for Structured SLAM Prieto-Fernández, Natalia Fernández-Blanco, Sergio Fernández-Blanco, Álvaro Benítez-Andrades, José Alberto Carro-De-Lorenzo, Francisco Benavides, Carmen Robotics One of the main challenges in simultaneous localization and mapping (SLAM) is real-time processing. High-computational loads linked to data acquisition and processing complicate this task. This article presents an efficient feature extraction approach for mapping structured environments. The proposed methodology, weighted conformal LiDAR-mapping (WCLM), is based on the extraction of polygonal profiles and propagation of uncertainties from raw measurement data. This is achieved using conformal M bius transformation. The algorithm has been validated experimentally using 2-D data obtained from a low-cost Light Detection and Ranging (LiDAR) range finder. The results obtained suggest that computational efficiency is significantly improved with reference to other state-of-the-art SLAM approaches. |
| title | Weighted Conformal LiDAR-Mapping for Structured SLAM |
| topic | Robotics |
| url | https://arxiv.org/abs/2402.03376 |