引用本文: 肖军浩, 施成浩, 黄开宏, 于清华. 单目相机—3维激光雷达的外参标定及融合里程计研究[J]. 机器人, 2021, 43(1): 17-28.
XIAO Junhao, SHI Chenghao, HUANG Kaihong, YU Qinghua. Extrinsic Calibration and Fused Odometry for Monocular Camera and 3D LiDAR[J]. ROBOT, 2021, 43(1): 17-28.
 Citation: XIAO Junhao, SHI Chenghao, HUANG Kaihong, YU Qinghua. Extrinsic Calibration and Fused Odometry for Monocular Camera and 3D LiDAR[J]. ROBOT, 2021, 43(1): 17-28.

## Extrinsic Calibration and Fused Odometry for Monocular Camera and 3D LiDAR

• 摘要: 针对单目相机与3维激光雷达的融合里程计问题，提出了双阶段外参标定方法和基于混合残差的融合里程计方法．双阶段相机-激光雷达外参标定结合了基于运动和基于互信息2种标定方法．第1阶段为基于运动的外参标定法，在无初值的情况下得到外参的粗估计．第2阶段为基于互信息的外参标定法，以第1阶段的结果作为初值，利用互信息原理校准激光雷达反射率和相机灰度值，来优化标定结果．为进一步提高标定精度，第2阶段采用了一种针对稀疏激光雷达点云的遮挡点检测方法．所提出的双阶段外参标定方法在无需预设初值的前提下保证了标定结果的精度．在此基础上，提出了一种基于混合残差的相机与激光雷达融合里程计方法．该方法同时利用图像的直接和非直接图像特征计算重投影残差和光度残差．然后将不同类型的残差统一到非线性优化框架下，实现里程计估计．针对激光雷达数据稀疏性带来的深度信息缺失的问题，提出了一种基于颜色信息的深度插值方法，有效补充了特征点数量．最后，基于实物和公共数据集实验，对所提出的外参标定和融合里程计算法的鲁棒性和精度进行了评估．实验结果表明，所提出的外参标定方法可以在没有初值的情况下，给出精确的外参估计；所提出的融合里程计方法在公共数据集上和实物实验中均表现出了良好的估计精度和鲁棒性．

Abstract: A two-stage extrinsic calibration method as well as a hybrid-residual-based odometry approach for monocular camera and 3D LiDAR (light detection and ranging) fusion are presented. The proposed two-stage camera-LiDAR extrinsic calibration method combines a motion-based approach and a mutual-information-based approach. At the first stage, a motion-based extrinsic parameter calibration approach is adopted to provide a coarse calibration result without a given initial guess. At the second stage, a mutual-information-based approach is adopted. Taking the results from the first stage as the initial values, the reflectivity information of LiDAR and the grey value information of camera images are registered based on the mutual information principle to refine the calibration result. To further improve the calibration accuracy, an occlusion detection algorithm is employed for sparse LiDAR point clouds at the second stage. The proposed two-stage extrinsic calibration method can guarantee a high calibration accuracy with no requirement for the initial guess. Based on the calibration, a hybrid-residual-based LiDAR-camera fused odometry is proposed. The proposed approach exploits both direct and indirect image features to calculate reprojection residuals and photometric residuals. The different residuals are then unified into a non-linear optimization framework for an odometry estimation. To deal with the depth information missing problem caused by sparse LiDAR data, a colour-based depth interpolation method is proposed, which effectively increases the number of feature points. Finally, experiments are conducted using both real-world and public datasets to evaluate the robustness and accuracy of the proposed extrinsic calibration and fused odometry algorithm. The results suggest that the proposed extrinsic calibration method can provide accurate extrinsic parameter estimation without initial values, and the fused odometry approach can achieve competitive estimation accuracy and robustness both on public and self-owned real-world datasets.

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