激光与光电子学进展, 2020, 57 (3): 030102, 网络出版: 2020-02-17   

基于3D特征点的激光雷达与立体视觉配准方法 下载: 1492次

Extrinsic Calibration for Lidar and Stereo Vision Using 3D Feature Points
陈少杰 1,2,*朱振才 3张永合 1郭明 1,**支帅 1,***
作者单位
1 中国科学院微小卫星创新研究院, 上海 201203
2 中国科学院大学, 北京 100049
3 国科学院微小卫星重点实验室, 上海 201203
摘要
激光雷达和双目相机作为无人驾驶领域中重要的环境感知设备,两者之间的外参配准是其联合应用的重要基础,然而两种信息的融合意味着繁琐的校准过程。基于此,提出一种基于特征点对匹配求解的方法,采用两块矩形木板,分别提取双目相机与激光雷达坐标系下的木板边缘3D点云,拟合空间直线求取角点坐标,最后利用Kabsch算法求解配对的特征点之间的坐标转换,利用聚类法去除多次测量结果中的异常值,并求取平均值。通过搭建实验,所提算法可在Nvidia Jetson Tx2嵌入式开发板上实现,获得了准确的配准参数,验证了理论方法的可行性。此配准方法简单易行,可自动完成多次测量,相比于同类方法精度也有所提高。
Abstract
Lidar and stereo cameras are important environmental sensors for unmanned driving. Calibrating external parameters between these two sensors is an important basis for their combination; however, combining two types of information requires a complex calibration process. This paper proposes a method based on feature point pair matching. Two rectangular planks are used to extract the 3D point cloud of the edge of the board in stereo vision and lidar coordinate systems, which is then used to obtain the corner coordinates. Finally, the Kabsch algorithm is used to solve the coordinate transformation between the paired feature points, and a clustering method is used to remove outliers from the multiple measurements and obtain the average value. By setting up an experiment, this method can be implemented on the Nvidia Jetson Tx2 embedded development board, and accurate registration parameters can be obtained, thus verifying the theoretical method’s feasibility. This registration method is simple and easy to execute, can automatically perform multiple measurements, and is improved compared with similar methods.

陈少杰, 朱振才, 张永合, 郭明, 支帅. 基于3D特征点的激光雷达与立体视觉配准方法[J]. 激光与光电子学进展, 2020, 57(3): 030102. Shaojie Chen, Zhencai Zhu, Yonghe Zhang, Ming Guo, Shuai Zhi. Extrinsic Calibration for Lidar and Stereo Vision Using 3D Feature Points[J]. Laser & Optoelectronics Progress, 2020, 57(3): 030102.

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