Ming Wei;Jun-Guo Lu;Nan Ye;Zhen Zhu;Qinghao Zhang;Yafei Wang
{"title":"自动驾驶中摄像头和激光雷达的全生命周期标定方法","authors":"Ming Wei;Jun-Guo Lu;Nan Ye;Zhen Zhu;Qinghao Zhang;Yafei Wang","doi":"10.1109/TIM.2025.3565783","DOIUrl":null,"url":null,"abstract":"In recent years, advancements in light laser detection and ranging (LiDAR) and camera technologies have brought increasing attention to autonomous driving. Sensor fusion provides complementary information that overcomes the limitations of individual sensors and improves the safety of autonomous vehicles. The key to achieving this fusion lies in sensor calibration. This article presents a full-lifecycle calibration method that integrates offline and online processes to achieve high-precision and efficient calibration for camera-LiDAR systems in autonomous driving. The proposed approach unifies the calibration stages, addressing both the initial factory calibration and the dynamic adjustments required during vehicle operation. Offline calibration is performed using specialized calibration boards and standardized workflows to establish accurate initial parameters, ensuring a robust foundation for subsequent operations. Online calibration leverages a learning-based, end-to-end deep declarative network to dynamically adjust calibration parameters in real time, compensating for sensor displacement caused by vibration, loosening, or collisions. Extensive experiments are conducted to validate the proposed method. The accuracy and robustness of the offline calibration are validated through quantitative and qualitative results in simulated and real-world tests. Competitive precision in online calibration is demonstrated on public datasets, with average translation errors of 0.667 and 1.937 cm, and average rotation errors of 0.149° and 0.138° achieved on the KITTI and NuScenes datasets, respectively. Additionally, the online calibration method’s real-time capability is confirmed in practical experiments, with a frame rate of approximately 11 frames/s. This full-lifecycle calibration method significantly improves the accuracy, reliability, and long-term stability of autonomous driving systems, addressing critical challenges in sensor calibration across diverse environments. The code will be released at <uri>https://github.com/weiming2/off-onlineCalib.git</uri>","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-17"},"PeriodicalIF":5.6000,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Full-Lifecycle Calibration Method for Camera and LiDAR in Autonomous Driving\",\"authors\":\"Ming Wei;Jun-Guo Lu;Nan Ye;Zhen Zhu;Qinghao Zhang;Yafei Wang\",\"doi\":\"10.1109/TIM.2025.3565783\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In recent years, advancements in light laser detection and ranging (LiDAR) and camera technologies have brought increasing attention to autonomous driving. Sensor fusion provides complementary information that overcomes the limitations of individual sensors and improves the safety of autonomous vehicles. The key to achieving this fusion lies in sensor calibration. This article presents a full-lifecycle calibration method that integrates offline and online processes to achieve high-precision and efficient calibration for camera-LiDAR systems in autonomous driving. The proposed approach unifies the calibration stages, addressing both the initial factory calibration and the dynamic adjustments required during vehicle operation. Offline calibration is performed using specialized calibration boards and standardized workflows to establish accurate initial parameters, ensuring a robust foundation for subsequent operations. Online calibration leverages a learning-based, end-to-end deep declarative network to dynamically adjust calibration parameters in real time, compensating for sensor displacement caused by vibration, loosening, or collisions. Extensive experiments are conducted to validate the proposed method. The accuracy and robustness of the offline calibration are validated through quantitative and qualitative results in simulated and real-world tests. Competitive precision in online calibration is demonstrated on public datasets, with average translation errors of 0.667 and 1.937 cm, and average rotation errors of 0.149° and 0.138° achieved on the KITTI and NuScenes datasets, respectively. Additionally, the online calibration method’s real-time capability is confirmed in practical experiments, with a frame rate of approximately 11 frames/s. This full-lifecycle calibration method significantly improves the accuracy, reliability, and long-term stability of autonomous driving systems, addressing critical challenges in sensor calibration across diverse environments. 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A Full-Lifecycle Calibration Method for Camera and LiDAR in Autonomous Driving
In recent years, advancements in light laser detection and ranging (LiDAR) and camera technologies have brought increasing attention to autonomous driving. Sensor fusion provides complementary information that overcomes the limitations of individual sensors and improves the safety of autonomous vehicles. The key to achieving this fusion lies in sensor calibration. This article presents a full-lifecycle calibration method that integrates offline and online processes to achieve high-precision and efficient calibration for camera-LiDAR systems in autonomous driving. The proposed approach unifies the calibration stages, addressing both the initial factory calibration and the dynamic adjustments required during vehicle operation. Offline calibration is performed using specialized calibration boards and standardized workflows to establish accurate initial parameters, ensuring a robust foundation for subsequent operations. Online calibration leverages a learning-based, end-to-end deep declarative network to dynamically adjust calibration parameters in real time, compensating for sensor displacement caused by vibration, loosening, or collisions. Extensive experiments are conducted to validate the proposed method. The accuracy and robustness of the offline calibration are validated through quantitative and qualitative results in simulated and real-world tests. Competitive precision in online calibration is demonstrated on public datasets, with average translation errors of 0.667 and 1.937 cm, and average rotation errors of 0.149° and 0.138° achieved on the KITTI and NuScenes datasets, respectively. Additionally, the online calibration method’s real-time capability is confirmed in practical experiments, with a frame rate of approximately 11 frames/s. This full-lifecycle calibration method significantly improves the accuracy, reliability, and long-term stability of autonomous driving systems, addressing critical challenges in sensor calibration across diverse environments. The code will be released at https://github.com/weiming2/off-onlineCalib.git
期刊介绍:
Papers are sought that address innovative solutions to the development and use of electrical and electronic instruments and equipment to measure, monitor and/or record physical phenomena for the purpose of advancing measurement science, methods, functionality and applications. The scope of these papers may encompass: (1) theory, methodology, and practice of measurement; (2) design, development and evaluation of instrumentation and measurement systems and components used in generating, acquiring, conditioning and processing signals; (3) analysis, representation, display, and preservation of the information obtained from a set of measurements; and (4) scientific and technical support to establishment and maintenance of technical standards in the field of Instrumentation and Measurement.