Calibration of the external parameters of cameras and LiDAR is a prerequisite for achieving high-precision fusion of multimodal data. Existing target-based calibration methods are found to compromise calibration accuracy due to the lack of distinct geometric features. Therefore, a joint external parameter calibration method for LiDAR and cameras, which is based on a centroid guidance strategy, was proposed. A multi-feature point calibration target with centroid constraints was designed, and the coordinate information of feature points was acquired from both point cloud and image data by integrating the geometric parameters of the calibration target. Furthermore, the Huber loss function was incorporated into the quasi-Newton method to further optimize the joint external parameters. Finally, 3D object detection was performed in real-world scenes to verify the effectiveness of the proposed method. The centroid guidance strategy was introduced. By improving the quasi-Newton algorithm for external parameter optimization, the average reprojection error is reduced by 8.4% compared with the four-feature point calibration method. Comparative experiments demonstrate that the improved quasi-Newton optimization algorithm has a beneficial effect on enhancing the accuracy of joint external parameters.
LIU Chao-shan, RAN Teng, HE Li, TAO Qing.
Joint Calibration Method of LiDAR and Camera Based on Centroid Guidance Strategy[J].
Science Technology and Engineering, 2026, 26(14): 6085-6095 DOI:10.12404/j.issn.1671-1815.2505318
相比之下,基于目标的离线标定方法通过已知特征或特定标定目标,能在受控环境下实现高精度外参标定。这类方法不依赖动态场景或复杂的运动估计,且计算效率较高,适用于传感器初始标定或需要高精度标定的场景。Zhang等[18]首先提出了一种基于棋盘格的标定方法,该方法易于实现,但无法获得最佳解决方案。后来,基于目标的标定方法着重开始探索几何约束下的特征。Zhang等[19]提出一种通过从点云与图像中提取角点来完成激光雷达与相机联合标定的方法,然后通过最大化法向向量与最小化点到平面的距离来计算联合外参。Geiger等[20]通过从点云与图像中提取目标顶点来完成激光雷达与相机联合标定,并通过2D-3D最小化欧氏距离来完成联合外参的优化问题。Huang等[21]利用棋盘格作为标定目标,通过算法组合线条与平面的对应关系,并将算法进行扩展,用于估计激光雷达与相机之间的相似性转换。Zhou等[22]通过标定板的几何形状作为参考,从激光雷达与相机中获取感兴趣的特征点,在完成标定任务后,利用遗传算法进行参数优化。Verma等[23]使用棋盘格作为标定目标,分别从图像和点云数据提取棋盘格的法向量和距离,通过非线性优化求解联合外参。Pandey等[24]也采用棋盘格作为标定目标,利用激光点云反射强度和棋盘格颜色之间的亮度值相关性测度计算完成联合标定。除使用棋盘格作为参数标定目标之外,有学者还尝试了其他方法。如Wang等[25]提出基于任意三面体的激光雷达与相机联合外参标定方法,通过任意三面体间的位置关系完成初始参数的标定,并且通过构建非线性最小二乘(nonlinear least square,NLS)优化问题对初始联合外参进行优化。Gong等[26]提出基于正交三面体的激光雷达与相机联合标定方法,该方法分为两部分标定过程,即激光雷达与机器人间坐标变换关系及激光雷达与相机间坐标变换关系,最后通过参数转换完成激光雷达与相机联合外参标定。Da等[27]提出基于多组多边形的激光雷达与相机联合标定方法,通过激光雷达3D数据估算标定板顶点的位置,并且在2D图像中找到对应位置,进而完成2D-3D匹配,获取标定参数。除上述方法外,董文波等[28]提出基于标靶内点与边缘角点双重约束的联合标定方法,建立基于内点与边缘角点的损失函数模型,进而完成标定。吴军等[29]提出一种基于背景聚类的联合标定方法,避免了在三维点云中盲目检测标定板点云,而导致标定结果存在较大误差的问题。通常所有基于目标的方法都需要已知参考来从两个传感器中提取几何特征,以形成优化问题。
LiHanlin, JiangYueming, AnTengfei, et al. Sewer robot localization system based on multi-sensor fusion[J]. Science Technology and Engineering, 2025, 25(26): 11252-11259.
LiuGuangcai, JinSongpeng, LiZhangping, et al. Path planning of urban manned eVTOL based on the improved artificial electric field algorithm[J]. Science Technology and Engineering, 2025, 25(1): 238-244.
ChaiJiahui, LiMinglei, LiMin, et al. ResCalib: geometry-supervised deep network for dynamic LiDAR-camera joint calibration[J]. Journal of Radars, 2025(14): 1-13.
SunHaoyang, WangZhiqiang, ShenChengwu, et al. A method for camera intrinsic parameters calibration based on subpixel iteration algorithm[J]. Science Technology and Engineering, 2025, 25(12): 5103-5109.
[9]
LevinsonJ, ThrunS. Automatic online calibration of cameras and lasers[C]//Proceedings of Robotics: Science and Systems. Cambridge: MIT Press, 2013: 10004324.
[10]
PandeyG, McBrideJ, SavareseS, et al. Automatic targetless extrinsic calibration of a 3D LiDAR and camera by maximizing mutual information[C]//Proceedings of the AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2012: 2053-2059.
[11]
KangJ, DohN L. Automatic targetless camera-LiDAR calibration by aligning edge with gaussian mixture model[J]. Journal of Field Robotics, 2020, 37(1): 158-179.
LiuWanli, LiuYang, ZhangXueliang. External parameter calibration method for LiDAR and camera by fusing mutual information and multi-feature constraints[J]. Chinese Journal of Scientific Instrument, 2025, 46(1): 29-41.
XuXiaobin, CaoChenfei, ZhangLei, et al. Calibration method for planar array LiDAR and camera based on tetrahedral features[J]. Acta Photonica Sinica, 2024, 53(7): 176-190.
[16]
IshikawaR, OishiT, IkeuchiK. LiDAR andcamera calibration using motions estimated by sensor fusion odometry[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). New York:IEEE, 2018: 7342-7349.
ZhangLei, XuXiaobin, HeJia, et al. Calibration method for 2D LiDAR and camera based on indoor structural features[J]. Acta Photonica Sinica, 2020, 49(12): 67-78.
[19]
HuangK, StachnissC. Extrinsic multi-sensor calibration for mobile robots using the gauss-helmert model[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). New York:IEEE, 2017: 1490-1499.
[20]
QiuK, QinT, PanJ, et al. Real-time temporal and rotational calibration of heterogeneous sensors using motion correlation analysis[J]. IEEE Transactions on Robotics, 2020, 37(2): 587-602.
ZhaoLiang, HuJie, LiuHan, et al. 3D object detection in LiDAR point clouds using semantic segmentation-based deep learning[J]. Chinese Journal of Lasers, 2021, 48(17): 177-189.
[23]
LiJ, HuM, LiuS, et al. Improved LiDAR-camera calibration based on hand-eye model under motion limitation[J]. IEEE Sensors Journal, 2023, 23(16): 18634-18643.
[24]
WuS, HadachiA, VivetD, et al. NetCalib:a novel approach for LiDAR-camera auto-calibration based on deep learning[C]// 25th International Conference on Pattern Recognition (ICPR). New York: IEEE, 2021: 6648-6655.
LiHan, GeDongyuan, YaoXifan. Deep neural network for fisheye camera calibration and distortion handling[J]. Science Technology and Engineering, 2025, 25(17): 7260-7267.
[27]
ZhangW, XuD. Extrinsic calibration of LiDAR-camera based on deep convolutional network[C]// China Automation Congress (CAC). New York: IEEE, 2022: 2949-2954.
[28]
ZhangQ, PlessR. Extrinsic calibration of a camera and laser range finder (improves camera calibration)[C]// IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). New York:IEEE, 2004: 2301-2306.
[29]
GeigerA, MoosmannF, CarÖ, et al. Automatic camera and range sensor calibration using a single shot[C]// IEEE International Conference on Robotics and Automation. New York:IEEE, 2012: 3936-3943.
[30]
HuangJ K, GrizzleJ W. Improvements to target-based 3D LiDAR to camera calibration[J]. IEEE Access, 2020, 8: 134101-134110.
[31]
ZhouL, LiZ, KaessM. Automatic extrinsic calibration of a camera and a 3D LiDAR using line and plane correspondences[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). New York: IEEE, 2018: 5562-5569.
[32]
VermaS, BerrioJ S, WorrallS, et al. Automatic extrinsic calibration between a camera and a 3D LiDAR using 3D point and plane correspondences[C]// IEEE Intelligent Transportation Systems Conference (ITSC). New York: IEEE, 2019: 3906-3912.
[33]
PandeyG, McBrideJ, SavareseS, et al. Extrinsic calibration of a 3D laser scanner and an omnidirectional camera[J]. IFAC Proceedings Volumes, 2010, 43(16): 336-341.
[34]
WangW, SakaradaK, KawaguchiN. Reflectance intensity assisted automatic and accurate extrinsic calibration of 3D LiDAR and panoramic camera using a printed chessboard[J]. Remote Sensing, 2017, 9(8): 851.
[35]
GongX, LinY, LiuJ. 3D LiDAR-camera extrinsic calibration using an arbitrary trihedron[J]. Sensors, 2013, 13(2): 1902-1918.
[36]
Da CruzG P, AzpúruaH, LizarraldeF, et al. Extrinsic LiDAR-robot and LiDAR-camera calibration for multi-sensor fusion[C]// Latin American Robotics Symposium (LARS). New York:IEEE, 2024: 1-6.
DongWenbo, YuHaipeng, YuLong. Joint calibration of LiDAR and camera based on dual constraints of target interior points and edge corner points[J]. Chinese Journal of Sensors and Actuators, 2023, 36(1): 30-35.
WuJun, YuanShaobo, ZhuYuheng, et al. Optimization method for LiDAR and camera external parameter calibration using adaptive background clustering[J]. Chinese Journal of Scientific Instrument, 2023, 44(2): 230-237.
[41]
YanG, HeF, ShiC, et al. Joint camera intrinsic and LiDAR-camera extrinsic calibration[C]// IEEE International Conference on Robotics and Automation (ICRA). New York: IEEE, 2023: 11446-11452.
LiXin, WangXiaoxia, YangFengbao. Extrinsic calibration method for lidar and camera based on feature point matching[J]. Modern Electronics Technique, 2025, 48(3): 19-25.
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