Laser & Optoelectronics Progress, Volume. 57, Issue 3, 030102(2020)
Extrinsic Calibration for Lidar and Stereo Vision Using 3D Feature Points
Fig. 1. SGBM algorithm framework
Fig. 2. Imaging system. (a) Geometric relationship between parallax and depth; (b) getting spatial points from pixel coordinates
Fig. 3. Experimental device diagram. (a) Board layout; (b) layout of lidar and binocular camera
Fig. 4. Edge extraction. (a) (b) Edge extraction results of different perspectives of binocular camera; (c) frame selection of lidar point cloud
Fig. 5. Point cloud fusion results from different perspectives in two scenes. (a) (b) Scene one; (c)(d) scene two
Fig. 6. Results of lidar point cloud reprojection
Fig. 7. Point cloud fusion of two different perspectives of binocular camera. (a)(b) Left view of two perspectives; (c) result of the point cloud fusion
Fig. 8. Fusion results of method in Ref. [4]. (a) Result of edge point extraction; (b) result of circle center extraction
Fig. 9. Fusion results of method in Ref. [4]. (a) Result 1; (b) another result
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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
Category: Atmospheric Optics and Oceanic Optics
Received: Jul. 10, 2019
Accepted: Jul. 29, 2019
Published Online: Feb. 17, 2020
The Author Email: Chen Shaojie (1084010191@qq.com), Guo Ming (googlm@163.com), Zhi Shuai (zhishuai0705@163.com)