Multi-Lidar System Localization and Mapping with Online Calibration
Currently, the demand for automobiles is increasing, and daily travel is increasingly reliant on cars. However, accompanying this trend are escalating traffic safety issues. Surveys indicate that most traffic accidents stem from driver errors, both intentional and unintentional. Consequently, within...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/18/10193 |
| _version_ | 1827727391485591552 |
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| author | Fang Wang Xilong Zhao Hengzhi Gu Lida Wang Siyu Wang Yi Han |
| author_facet | Fang Wang Xilong Zhao Hengzhi Gu Lida Wang Siyu Wang Yi Han |
| author_sort | Fang Wang |
| collection | DOAJ |
| description | Currently, the demand for automobiles is increasing, and daily travel is increasingly reliant on cars. However, accompanying this trend are escalating traffic safety issues. Surveys indicate that most traffic accidents stem from driver errors, both intentional and unintentional. Consequently, within the framework of vehicular intelligence, intelligent driving uses computer software to assist drivers, thereby reducing the likelihood of road safety incidents and traffic accidents. Lidar, an essential facet of perception technology, plays an important role in vehicle intelligent driving. In real-world driving scenarios, the detection range of a single laser radar is limited. Multiple laser radars can improve the detection range and point density, effectively mitigating state estimation degradation in unstructured environments. This, in turn, enhances the precision and accuracy of synchronous positioning and mapping. Nonetheless, the relationship governing pose transformation between multiple lidars is intricate. Over extended periods, perturbations arising from vibrations, temperature fluctuations, or collisions can compromise the initially converged external parameters. In view of these concerns, this paper introduces a system capable of concurrent multi-lidar positioning and mapping, as well as real-time online external parameter calibration. The method first preprocesses the original measurement data, extracts linear and planar features, and rectifies motion distortion. Subsequently, leveraging degradation factors, the convergence of the multi-lidar external parameters is detected in real time. When deterioration in external parameters is identified, the local map of the main laser radar and the feature point cloud of the auxiliary laser radar are associated to realize online calibration. This is succeeded by frame-to-frame matching according to the converged external parameters, culminating in laser odometer computation. Introducing ground constraints and loop closure detection constraints in the back-end optimization effectuates global estimated pose rectification. Concurrently, the feature point cloud is aligned with the global map, and map update is completed. Finally, experimental validation is conducted on data acquired from Chang’an University to substantiate the system’s online calibration and positioning mapping accuracy, robustness, and real-time performance. |
| first_indexed | 2024-03-10T23:05:00Z |
| format | Article |
| id | doaj.art-b85b425a0d6e42b7a52931bde6ce707f |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T23:05:00Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-b85b425a0d6e42b7a52931bde6ce707f2023-11-19T09:24:17ZengMDPI AGApplied Sciences2076-34172023-09-0113181019310.3390/app131810193Multi-Lidar System Localization and Mapping with Online CalibrationFang Wang0Xilong Zhao1Hengzhi Gu2Lida Wang3Siyu Wang4Yi Han5Department of Automotive Engineering, Xi’an Vocational University of Automobile, Xi’an 710038, ChinaDepartment of Automobile, Chang’an University, Xi’an 710064, ChinaDepartment of Automobile, Chang’an University, Xi’an 710064, ChinaDepartment of Automotive Engineering, Xi’an Vocational University of Automobile, Xi’an 710038, ChinaDepartment of Automobile, Chang’an University, Xi’an 710064, ChinaDepartment of Automobile, Chang’an University, Xi’an 710064, ChinaCurrently, the demand for automobiles is increasing, and daily travel is increasingly reliant on cars. However, accompanying this trend are escalating traffic safety issues. Surveys indicate that most traffic accidents stem from driver errors, both intentional and unintentional. Consequently, within the framework of vehicular intelligence, intelligent driving uses computer software to assist drivers, thereby reducing the likelihood of road safety incidents and traffic accidents. Lidar, an essential facet of perception technology, plays an important role in vehicle intelligent driving. In real-world driving scenarios, the detection range of a single laser radar is limited. Multiple laser radars can improve the detection range and point density, effectively mitigating state estimation degradation in unstructured environments. This, in turn, enhances the precision and accuracy of synchronous positioning and mapping. Nonetheless, the relationship governing pose transformation between multiple lidars is intricate. Over extended periods, perturbations arising from vibrations, temperature fluctuations, or collisions can compromise the initially converged external parameters. In view of these concerns, this paper introduces a system capable of concurrent multi-lidar positioning and mapping, as well as real-time online external parameter calibration. The method first preprocesses the original measurement data, extracts linear and planar features, and rectifies motion distortion. Subsequently, leveraging degradation factors, the convergence of the multi-lidar external parameters is detected in real time. When deterioration in external parameters is identified, the local map of the main laser radar and the feature point cloud of the auxiliary laser radar are associated to realize online calibration. This is succeeded by frame-to-frame matching according to the converged external parameters, culminating in laser odometer computation. Introducing ground constraints and loop closure detection constraints in the back-end optimization effectuates global estimated pose rectification. Concurrently, the feature point cloud is aligned with the global map, and map update is completed. Finally, experimental validation is conducted on data acquired from Chang’an University to substantiate the system’s online calibration and positioning mapping accuracy, robustness, and real-time performance.https://www.mdpi.com/2076-3417/13/18/10193SLAMmultiple lidarscalibration |
| spellingShingle | Fang Wang Xilong Zhao Hengzhi Gu Lida Wang Siyu Wang Yi Han Multi-Lidar System Localization and Mapping with Online Calibration Applied Sciences SLAM multiple lidars calibration |
| title | Multi-Lidar System Localization and Mapping with Online Calibration |
| title_full | Multi-Lidar System Localization and Mapping with Online Calibration |
| title_fullStr | Multi-Lidar System Localization and Mapping with Online Calibration |
| title_full_unstemmed | Multi-Lidar System Localization and Mapping with Online Calibration |
| title_short | Multi-Lidar System Localization and Mapping with Online Calibration |
| title_sort | multi lidar system localization and mapping with online calibration |
| topic | SLAM multiple lidars calibration |
| url | https://www.mdpi.com/2076-3417/13/18/10193 |
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