Hong Kong UrbanNav: An Open-Source Multisensory Dataset for Benchmarking Urban Navigation Algorithms
Accurate positioning in urban canyons remains a challenging problem. To facilitate the research and development of reliable and precise positioning methods using multiple sensors in urban canyons, we built a multisensory dataset, UrbanNav, collected in diverse, challenging urban scenarios in Hong Ko...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Institute of Navigation
2023-08-01
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| Series: | Navigation |
| Online Access: | https://navi.ion.org/content/70/4/navi.602 |
| _version_ | 1797393306138705920 |
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| author | Li-Ta Hsu Feng Huang Hoi-Fung Ng Guohao Zhang Yihan Zhong Xiwei Bai Weisong Wen |
| author_facet | Li-Ta Hsu Feng Huang Hoi-Fung Ng Guohao Zhang Yihan Zhong Xiwei Bai Weisong Wen |
| author_sort | Li-Ta Hsu |
| collection | DOAJ |
| description | Accurate positioning in urban canyons remains a challenging problem. To facilitate the research and development of reliable and precise positioning methods using multiple sensors in urban canyons, we built a multisensory dataset, UrbanNav, collected in diverse, challenging urban scenarios in Hong Kong. The dataset provides multi-sensor data, including data from multi-frequency global navigation satellite system (GNSS) receivers, an inertial measurement unit (IMU), multiple light detection and ranging (lidar) units, and cameras. Meanwhile, the ground truth of the positioning (with centimeter-level accuracy) is postprocessed by commercial software from NovAtel using an integrated GNSS real-time kinematic and fiber optics gyroscope inertial system. In this paper, the sensor systems, spatial and temporal calibration, data formats, and scenario descriptions are presented in detail. Meanwhile, the benchmark performance of several existing positioning methods is provided as a baseline. Based on the evaluations, we conclude that GNSS can provide satisfactory results in a middle-class urban canyon if an appropriate receiver and algorithms are applied. Both visual and lidar odometry are satisfactory in deep urban canyons, whereas tunnels are still a major challenge. Multisensory integration with the aid of an IMU is a promising solution for achieving seamless positioning in cities. The dataset in its entirety can be found on GitHub at https://github.com/IPNL-POLYU/UrbanNavDataset. |
| first_indexed | 2024-03-09T00:01:33Z |
| format | Article |
| id | doaj.art-0ea504d0d408498ab9b92c4750cd7a01 |
| institution | Directory Open Access Journal |
| issn | 2161-4296 |
| language | English |
| last_indexed | 2024-03-09T00:01:33Z |
| publishDate | 2023-08-01 |
| publisher | Institute of Navigation |
| record_format | Article |
| series | Navigation |
| spelling | doaj.art-0ea504d0d408498ab9b92c4750cd7a012023-12-12T17:32:42ZengInstitute of NavigationNavigation2161-42962023-08-0170410.33012/navi.602navi.602Hong Kong UrbanNav: An Open-Source Multisensory Dataset for Benchmarking Urban Navigation AlgorithmsLi-Ta HsuFeng HuangHoi-Fung NgGuohao ZhangYihan ZhongXiwei BaiWeisong WenAccurate positioning in urban canyons remains a challenging problem. To facilitate the research and development of reliable and precise positioning methods using multiple sensors in urban canyons, we built a multisensory dataset, UrbanNav, collected in diverse, challenging urban scenarios in Hong Kong. The dataset provides multi-sensor data, including data from multi-frequency global navigation satellite system (GNSS) receivers, an inertial measurement unit (IMU), multiple light detection and ranging (lidar) units, and cameras. Meanwhile, the ground truth of the positioning (with centimeter-level accuracy) is postprocessed by commercial software from NovAtel using an integrated GNSS real-time kinematic and fiber optics gyroscope inertial system. In this paper, the sensor systems, spatial and temporal calibration, data formats, and scenario descriptions are presented in detail. Meanwhile, the benchmark performance of several existing positioning methods is provided as a baseline. Based on the evaluations, we conclude that GNSS can provide satisfactory results in a middle-class urban canyon if an appropriate receiver and algorithms are applied. Both visual and lidar odometry are satisfactory in deep urban canyons, whereas tunnels are still a major challenge. Multisensory integration with the aid of an IMU is a promising solution for achieving seamless positioning in cities. The dataset in its entirety can be found on GitHub at https://github.com/IPNL-POLYU/UrbanNavDataset.https://navi.ion.org/content/70/4/navi.602 |
| spellingShingle | Li-Ta Hsu Feng Huang Hoi-Fung Ng Guohao Zhang Yihan Zhong Xiwei Bai Weisong Wen Hong Kong UrbanNav: An Open-Source Multisensory Dataset for Benchmarking Urban Navigation Algorithms Navigation |
| title | Hong Kong UrbanNav: An Open-Source Multisensory Dataset for Benchmarking Urban Navigation Algorithms |
| title_full | Hong Kong UrbanNav: An Open-Source Multisensory Dataset for Benchmarking Urban Navigation Algorithms |
| title_fullStr | Hong Kong UrbanNav: An Open-Source Multisensory Dataset for Benchmarking Urban Navigation Algorithms |
| title_full_unstemmed | Hong Kong UrbanNav: An Open-Source Multisensory Dataset for Benchmarking Urban Navigation Algorithms |
| title_short | Hong Kong UrbanNav: An Open-Source Multisensory Dataset for Benchmarking Urban Navigation Algorithms |
| title_sort | hong kong urbannav an open source multisensory dataset for benchmarking urban navigation algorithms |
| url | https://navi.ion.org/content/70/4/navi.602 |
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