WHU-helmet: a helmet-based multisensor SLAM dataset for the evaluation of real-time 3-D mapping in large-scale GNSS-denied environments
Real-time 3-D mapping of large-scale global navigation satellite system (GNSS)-denied environments plays an important role in forest inventory management, disaster emergency response, and underground facility maintenance. Compact helmet laser scanning (HLS) systems keep the same direction as the use...
| Main Authors: | , , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/172258 |
| _version_ | 1826114570620502016 |
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| author | Li, Jianping Wu, Weitong Yang, Bisheng Zou, Xianghong Yang, Yandi Zhao, Xin Dong, Zhen |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Li, Jianping Wu, Weitong Yang, Bisheng Zou, Xianghong Yang, Yandi Zhao, Xin Dong, Zhen |
| author_sort | Li, Jianping |
| collection | NTU |
| description | Real-time 3-D mapping of large-scale global navigation satellite system (GNSS)-denied environments plays an important role in forest inventory management, disaster emergency response, and underground facility maintenance. Compact helmet laser scanning (HLS) systems keep the same direction as the user's line of sight and have the advantage of 'what you see is what you get,' providing a promising and efficient solution for 3-D geospatial information acquisition. However, the violent motion of the helmet, the limited field of view (FoV) of the laser scanner, and the repeated symmetrical geometric structures in the GNSS-denied environments pose enormous challenges for the existing simultaneous localization and mapping (SLAM) algorithms. To promote the development of HLS and explore its application in large-scale GNSS-denied environments, the first large-scale HLS dataset covering multiple difficult GNSS-denied areas (e.g., forests, mountains, underground spaces) was built in this study. Besides using an additional very high accuracy fiber-optic inertial measurement unit (IMU), a novel postprocessing multisource fusion method - progressive trajectory correction (PTC) - is proposed to generate a reliable ground-truth trajectory for the benchmark, which overcomes the problems of scan matching degradation and nonrigid distortion. The accuracies of the ground truth are controlled and checked by manually surveyed feature points along the trajectory. Finally, the existing state-of-the-art SLAM methods were evaluated on the WHU-Helmet dataset, summarizing the future HLS SLAM research trends. The full dataset is available for download at: https://github.com/kafeiyin00/WHU-HelmetDataset. |
| first_indexed | 2024-10-01T03:41:15Z |
| format | Journal Article |
| id | ntu-10356/172258 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:41:15Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-10356/1722582024-07-11T04:14:29Z WHU-helmet: a helmet-based multisensor SLAM dataset for the evaluation of real-time 3-D mapping in large-scale GNSS-denied environments Li, Jianping Wu, Weitong Yang, Bisheng Zou, Xianghong Yang, Yandi Zhao, Xin Dong, Zhen School of Electrical and Electronic Engineering Benchmark Dataset Helmet Laser Scanning Real-time 3-D mapping of large-scale global navigation satellite system (GNSS)-denied environments plays an important role in forest inventory management, disaster emergency response, and underground facility maintenance. Compact helmet laser scanning (HLS) systems keep the same direction as the user's line of sight and have the advantage of 'what you see is what you get,' providing a promising and efficient solution for 3-D geospatial information acquisition. However, the violent motion of the helmet, the limited field of view (FoV) of the laser scanner, and the repeated symmetrical geometric structures in the GNSS-denied environments pose enormous challenges for the existing simultaneous localization and mapping (SLAM) algorithms. To promote the development of HLS and explore its application in large-scale GNSS-denied environments, the first large-scale HLS dataset covering multiple difficult GNSS-denied areas (e.g., forests, mountains, underground spaces) was built in this study. Besides using an additional very high accuracy fiber-optic inertial measurement unit (IMU), a novel postprocessing multisource fusion method - progressive trajectory correction (PTC) - is proposed to generate a reliable ground-truth trajectory for the benchmark, which overcomes the problems of scan matching degradation and nonrigid distortion. The accuracies of the ground truth are controlled and checked by manually surveyed feature points along the trajectory. Finally, the existing state-of-the-art SLAM methods were evaluated on the WHU-Helmet dataset, summarizing the future HLS SLAM research trends. The full dataset is available for download at: https://github.com/kafeiyin00/WHU-HelmetDataset. This work was supported in part by the National Natural Science Foundation Project under Grant 42130105 and Grant 42201477, in part by the National Science Fund for Distinguished Young Scholars under Grant 41725005, in part by the China Postdoctoral Science Foundation under Grant 2022M712441 and Grant 2022TQ0234, and in part by the Laboratory Independent Research Project of the State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS). 2023-12-04T05:51:02Z 2023-12-04T05:51:02Z 2023 Journal Article Li, J., Wu, W., Yang, B., Zou, X., Yang, Y., Zhao, X. & Dong, Z. (2023). WHU-helmet: a helmet-based multisensor SLAM dataset for the evaluation of real-time 3-D mapping in large-scale GNSS-denied environments. IEEE Transactions On Geoscience and Remote Sensing, 61, 3275307-. https://dx.doi.org/10.1109/TGRS.2023.3275307 0196-2892 https://hdl.handle.net/10356/172258 10.1109/TGRS.2023.3275307 2-s2.0-85159812954 61 3275307 en IEEE Transactions on Geoscience and Remote Sensing 10.21979/N9/OYIYZV © 2023 IEEE. All rights reserved. |
| spellingShingle | Benchmark Dataset Helmet Laser Scanning Li, Jianping Wu, Weitong Yang, Bisheng Zou, Xianghong Yang, Yandi Zhao, Xin Dong, Zhen WHU-helmet: a helmet-based multisensor SLAM dataset for the evaluation of real-time 3-D mapping in large-scale GNSS-denied environments |
| title | WHU-helmet: a helmet-based multisensor SLAM dataset for the evaluation of real-time 3-D mapping in large-scale GNSS-denied environments |
| title_full | WHU-helmet: a helmet-based multisensor SLAM dataset for the evaluation of real-time 3-D mapping in large-scale GNSS-denied environments |
| title_fullStr | WHU-helmet: a helmet-based multisensor SLAM dataset for the evaluation of real-time 3-D mapping in large-scale GNSS-denied environments |
| title_full_unstemmed | WHU-helmet: a helmet-based multisensor SLAM dataset for the evaluation of real-time 3-D mapping in large-scale GNSS-denied environments |
| title_short | WHU-helmet: a helmet-based multisensor SLAM dataset for the evaluation of real-time 3-D mapping in large-scale GNSS-denied environments |
| title_sort | whu helmet a helmet based multisensor slam dataset for the evaluation of real time 3 d mapping in large scale gnss denied environments |
| topic | Benchmark Dataset Helmet Laser Scanning |
| url | https://hdl.handle.net/10356/172258 |
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