Lyu Xinyu, Liu Songlin, Qiao Rongcan, Jiang Songyang, Wang Yuanshi
School of Computer, Qufu Normal University, Rizhao 276800, China.
Fifth Faculty, Information Engineering University China, Zhengzhou 450013, China.
Sensors (Basel). 2025 Sep 2;25(17):5409. doi: 10.3390/s25175409.
Multi-sensor fusion systems involving Light Detection and Ranging (LiDAR), cameras, and inertial measurement units (IMUs) have been widely adopted in fields such as autonomous driving and robotics due to their complementary perception capabilities. This widespread application has led to a growing demand for accurate sensor calibration. Although numerous calibration methods have been proposed in recent years for various sensor combinations, such as camera-IMU, LiDAR-IMU, camera-LiDAR, and camera-LiDAR-IMU, there remains a lack of systematic reviews and comparative analyses of these approaches. This paper focuses on extrinsic calibration techniques for LiDAR, cameras, and IMU, providing a comprehensive review of the latest developments across the four types of sensor combinations. We further analyze the strengths and limitations of existing methods, summarize the evaluation criteria for calibration, and outline potential future research directions for the benefit of the academic community.
涉及激光雷达（LiDAR）、摄像头和惯性测量单元（IMU）的多传感器融合系统，因其互补的感知能力，已在自动驾驶和机器人技术等领域得到广泛应用。这种广泛应用导致对精确传感器校准的需求不断增长。尽管近年来针对各种传感器组合，如摄像头-IMU、激光雷达-IMU、摄像头-激光雷达以及摄像头-激光雷达-IMU，已经提出了众多校准方法，但对这些方法仍缺乏系统的综述和比较分析。本文聚焦于激光雷达、摄像头和IMU的外部校准技术，全面综述了四种传感器组合的最新进展。我们进一步分析了现有方法的优缺点，总结了校准的评估标准，并为学术界勾勒了潜在的未来研究方向。
