Solid-state LiDAR and IMU coupled urban road non-revisiting mapping
3D mapping provides highly accurate environmental data, which is essential for critical applications such as autonomous driving and urban emergency response. Light detection and ranging (LiDAR) sensors, particularly solid-state ones, play a pivotal role in spatial–temporal mapping by providing preci...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-11-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843224005636 |
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| author | Xiaolong Ma Chun Liu Akram Akbar Yuanfan Qi Xiaohang Shao Yihong Qiao Xuefei Shao |
| author_facet | Xiaolong Ma Chun Liu Akram Akbar Yuanfan Qi Xiaohang Shao Yihong Qiao Xuefei Shao |
| author_sort | Xiaolong Ma |
| collection | DOAJ |
| description | 3D mapping provides highly accurate environmental data, which is essential for critical applications such as autonomous driving and urban emergency response. Light detection and ranging (LiDAR) sensors, particularly solid-state ones, play a pivotal role in spatial–temporal mapping by providing precise three-dimensional data of the environment, significantly enhancing remote sensing capabilities and adaptability to challenging environments compared to mechanical LiDAR systems. However, the limited field of view results in a sparse point cloud frame with few features, which poses challenges to feature matching, causes pose offset, and hinders spatial–temporal continuity, and further significant obstacle for existing vehicle-mounted mobile mapping methods. To address the above issues, we proposed a novel approach that integrating inertial measurement unit (IMU) with solid-state LiDAR. Specifically, it comprises two key modules: an initial localization mapping module, mitigating the limitations of solid-state LiDAR in positioning and mapping accuracy, and an attitude optimization mapping module utilizing real-time high-frequency IMU data to identify key frames for correcting initial attitudes and generating accurate 3D maps. The effectiveness of the method is validated through extensive experiments in complex community and high-speed urban road scenarios. Furthermore, our approach outperforms than the state-of-the-art techniques in test scenarios, achieving a significant 35% reduction in average absolute pose error and enhancing the robustness of vehicle-mounted mapping. |
| format | Article |
| id | doaj-art-c13bba4e60bc4bd58c2d96ffeab4750a |
| institution | Kabale University |
| issn | 1569-8432 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-c13bba4e60bc4bd58c2d96ffeab4750a2024-11-16T05:10:14ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322024-11-01134104207Solid-state LiDAR and IMU coupled urban road non-revisiting mappingXiaolong Ma0Chun Liu1Akram Akbar2Yuanfan Qi3Xiaohang Shao4Yihong Qiao5Xuefei Shao6College of Surveying and Geo-informatics, Tongji University, Shanghai 200092, ChinaCollege of Surveying and Geo-informatics, Tongji University, Shanghai 200092, China; Corresponding author.College of Surveying and Geo-informatics, Tongji University, Shanghai 200092, China; College of Electronic and Information Engineering, Tongji University, ChinaCollege of Surveying and Geo-informatics, Tongji University, Shanghai 200092, ChinaCollege of Surveying and Geo-informatics, Tongji University, Shanghai 200092, ChinaCollege of Surveying and Geo-informatics, Tongji University, Shanghai 200092, ChinaAutonomous Driving Center, SAIC MOTOR R&D Innovation Headquarters, Shanghai 201804, China3D mapping provides highly accurate environmental data, which is essential for critical applications such as autonomous driving and urban emergency response. Light detection and ranging (LiDAR) sensors, particularly solid-state ones, play a pivotal role in spatial–temporal mapping by providing precise three-dimensional data of the environment, significantly enhancing remote sensing capabilities and adaptability to challenging environments compared to mechanical LiDAR systems. However, the limited field of view results in a sparse point cloud frame with few features, which poses challenges to feature matching, causes pose offset, and hinders spatial–temporal continuity, and further significant obstacle for existing vehicle-mounted mobile mapping methods. To address the above issues, we proposed a novel approach that integrating inertial measurement unit (IMU) with solid-state LiDAR. Specifically, it comprises two key modules: an initial localization mapping module, mitigating the limitations of solid-state LiDAR in positioning and mapping accuracy, and an attitude optimization mapping module utilizing real-time high-frequency IMU data to identify key frames for correcting initial attitudes and generating accurate 3D maps. The effectiveness of the method is validated through extensive experiments in complex community and high-speed urban road scenarios. Furthermore, our approach outperforms than the state-of-the-art techniques in test scenarios, achieving a significant 35% reduction in average absolute pose error and enhancing the robustness of vehicle-mounted mapping.http://www.sciencedirect.com/science/article/pii/S1569843224005636Solid state LiDARVehicle-mounted mapping systemUrban road sceneKeyframePosture optimization |
| spellingShingle | Xiaolong Ma Chun Liu Akram Akbar Yuanfan Qi Xiaohang Shao Yihong Qiao Xuefei Shao Solid-state LiDAR and IMU coupled urban road non-revisiting mapping International Journal of Applied Earth Observations and Geoinformation Solid state LiDAR Vehicle-mounted mapping system Urban road scene Keyframe Posture optimization |
| title | Solid-state LiDAR and IMU coupled urban road non-revisiting mapping |
| title_full | Solid-state LiDAR and IMU coupled urban road non-revisiting mapping |
| title_fullStr | Solid-state LiDAR and IMU coupled urban road non-revisiting mapping |
| title_full_unstemmed | Solid-state LiDAR and IMU coupled urban road non-revisiting mapping |
| title_short | Solid-state LiDAR and IMU coupled urban road non-revisiting mapping |
| title_sort | solid state lidar and imu coupled urban road non revisiting mapping |
| topic | Solid state LiDAR Vehicle-mounted mapping system Urban road scene Keyframe Posture optimization |
| url | http://www.sciencedirect.com/science/article/pii/S1569843224005636 |
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