An Efficient Calibration Approach for Arbitrary Equipped 3-D LiDAR Based on an Orthogonal Normal Vector Pair
| Autor: | Tao Wu, Deyuan Meng, Jian Li, Erke Shang, Meiping Shi, Xiangjing An |
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| Rok vydání: | 2014 |
| Předmět: |
Unmanned ground vehicle
Calibration (statistics) Computer science Mechanical Engineering Point cloud Ranging Industrial and Manufacturing Engineering Object detection Lidar Artificial Intelligence Control and Systems Engineering Electrical and Electronic Engineering Algorithm Normal Software Simulation Ground plane |
| Zdroj: | Journal of Intelligent & Robotic Systems. 79:21-36 |
| ISSN: | 1573-0409 0921-0296 |
| DOI: | 10.1007/s10846-014-0080-3 |
| Popis: | Light Detection And Ranging (LiDAR) has been widely employed in Unmanned Ground Vehicle (UGV) for autonomous navigation and object detection. In this paper, an efficient extrinsic parameter calibration approach, which is based on a pair of orthogonal normal vectors, is presented for an arbitrary equipped 3-D LiDAR. With the proposed approach, the whole calibration process can be easily and efficiently implemented in outdoor urban environment and no calibration equipment is required. The main advantages of this approach are twofold: (1) compared with traditional ways, the proposed approach employs an orthogonal normal vector pair, which is generated by ground plane and vertical wall in urban environment, so calibration equipments are not required anymore; (2) the normal vector is estimated from the point cloud data on a surface, thus a quite robust and accuracy estimation can be obtained. Experiments illustrate the effective and efficient performance of the proposed approach, compared with the state of the art. |
| Databáze: | OpenAIRE |
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