Large-Scale LiDAR SLAM with Factor Graph Optimization on High-Level Geometric Features
Although visual SLAM (simultaneous localization and mapping) methods obtain very accurate results using optimization of residual errors defined with respect to the matching features, the SLAM systems based on 3-D laser (LiDAR) data commonly employ variants of the iterative closest points algorithm a...
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MDPI AG
2021-05-01
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| Online Access: | https://www.mdpi.com/1424-8220/21/10/3445 |
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| author | Krzysztof Ćwian Michał R. Nowicki Jan Wietrzykowski Piotr Skrzypczyński |
| author_facet | Krzysztof Ćwian Michał R. Nowicki Jan Wietrzykowski Piotr Skrzypczyński |
| author_sort | Krzysztof Ćwian |
| collection | DOAJ |
| description | Although visual SLAM (simultaneous localization and mapping) methods obtain very accurate results using optimization of residual errors defined with respect to the matching features, the SLAM systems based on 3-D laser (LiDAR) data commonly employ variants of the iterative closest points algorithm and raw point clouds as the map representation. However, it is possible to extract from point clouds features that are more spatially extended and more meaningful than points: line segments and/or planar patches. In particular, such features provide a natural way to represent human-made environments, such as urban and mixed indoor/outdoor scenes. In this paper, we perform an analysis of the advantages of a LiDAR-based SLAM that employs high-level geometric features in large-scale urban environments. We present a new approach to the LiDAR SLAM that uses planar patches and line segments for map representation and employs factor graph optimization typical to state-of-the-art visual SLAM for the final map and trajectory optimization. The new map structure and matching of features make it possible to implement in our system an efficient loop closure method, which exploits learned descriptors for place recognition and factor graph for optimization. With these improvements, the overall software structure is based on the proven LOAM concept to ensure real-time operation. A series of experiments were performed to compare the proposed solution to the open-source LOAM, considering different approaches to loop closure computation. The results are compared using standard metrics of trajectory accuracy, focusing on the final quality of the estimated trajectory and the consistency of the environment map. With some well-discussed reservations, our results demonstrate the gains due to using the high-level features in the full-optimization approach in the large-scale LiDAR SLAM. |
| first_indexed | 2024-03-10T11:23:29Z |
| format | Article |
| id | doaj.art-fbef02f42d1a49e7909db1ad62cca55e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T11:23:29Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-fbef02f42d1a49e7909db1ad62cca55e2023-11-21T19:51:40ZengMDPI AGSensors1424-82202021-05-012110344510.3390/s21103445Large-Scale LiDAR SLAM with Factor Graph Optimization on High-Level Geometric FeaturesKrzysztof Ćwian0Michał R. Nowicki1Jan Wietrzykowski2Piotr Skrzypczyński3Institute of Robotics and Machine Intelligence, Poznan University of Technology, 60-965 Poznan, PolandInstitute of Robotics and Machine Intelligence, Poznan University of Technology, 60-965 Poznan, PolandInstitute of Robotics and Machine Intelligence, Poznan University of Technology, 60-965 Poznan, PolandInstitute of Robotics and Machine Intelligence, Poznan University of Technology, 60-965 Poznan, PolandAlthough visual SLAM (simultaneous localization and mapping) methods obtain very accurate results using optimization of residual errors defined with respect to the matching features, the SLAM systems based on 3-D laser (LiDAR) data commonly employ variants of the iterative closest points algorithm and raw point clouds as the map representation. However, it is possible to extract from point clouds features that are more spatially extended and more meaningful than points: line segments and/or planar patches. In particular, such features provide a natural way to represent human-made environments, such as urban and mixed indoor/outdoor scenes. In this paper, we perform an analysis of the advantages of a LiDAR-based SLAM that employs high-level geometric features in large-scale urban environments. We present a new approach to the LiDAR SLAM that uses planar patches and line segments for map representation and employs factor graph optimization typical to state-of-the-art visual SLAM for the final map and trajectory optimization. The new map structure and matching of features make it possible to implement in our system an efficient loop closure method, which exploits learned descriptors for place recognition and factor graph for optimization. With these improvements, the overall software structure is based on the proven LOAM concept to ensure real-time operation. A series of experiments were performed to compare the proposed solution to the open-source LOAM, considering different approaches to loop closure computation. The results are compared using standard metrics of trajectory accuracy, focusing on the final quality of the estimated trajectory and the consistency of the environment map. With some well-discussed reservations, our results demonstrate the gains due to using the high-level features in the full-optimization approach in the large-scale LiDAR SLAM.https://www.mdpi.com/1424-8220/21/10/34453-D LiDARSLAMgeometric featuresoptimizationbundle adjustment |
| spellingShingle | Krzysztof Ćwian Michał R. Nowicki Jan Wietrzykowski Piotr Skrzypczyński Large-Scale LiDAR SLAM with Factor Graph Optimization on High-Level Geometric Features Sensors 3-D LiDAR SLAM geometric features optimization bundle adjustment |
| title | Large-Scale LiDAR SLAM with Factor Graph Optimization on High-Level Geometric Features |
| title_full | Large-Scale LiDAR SLAM with Factor Graph Optimization on High-Level Geometric Features |
| title_fullStr | Large-Scale LiDAR SLAM with Factor Graph Optimization on High-Level Geometric Features |
| title_full_unstemmed | Large-Scale LiDAR SLAM with Factor Graph Optimization on High-Level Geometric Features |
| title_short | Large-Scale LiDAR SLAM with Factor Graph Optimization on High-Level Geometric Features |
| title_sort | large scale lidar slam with factor graph optimization on high level geometric features |
| topic | 3-D LiDAR SLAM geometric features optimization bundle adjustment |
| url | https://www.mdpi.com/1424-8220/21/10/3445 |
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