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Parameters Separated Calibration Based on Particle Swarm Optimization for a Camera and a Laser-Rangefinder Information Fusion

机译:基于粒子群优化的相机参数分离校准与激光测距仪信息融合

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Heterogeneous sensors fusion of a camera and a laser-rangefinder can greatly improve the environment perception ability, and its primary problem is the calibration of depth scan and image information. At first, the mapping relationship among world coordinate system, camera coordinate system, and image plane is discussed, and then the calibration of camera intrinsic parameters is achieved. Moreover, the intrinsic and extrinsic parameters separated calibration is presented for a camera and a laser-rangefinder, and the characteristic identification is adopted by two intersection calibration boards with a certain angel for fusion characters extraction. Furthermore, the particle swarm optimization is proposed for the extrinsic parameters estimation with different objectives, and the Gaussian elimination is utilized for the initial particle swarm. The simulation and real experimental results show that the standard deviation of calibration error in the 21-group experiments is decreased by 10.175%, and it also proves the accuracy and effectiveness of our approaches.
机译:相机和激光测距仪的异构传感器融合可以极大地提高环境感知能力,其主要问题是深度扫描和图像信息的校准。首先讨论了世界坐标系,摄像机坐标系和像平面之间的映射关系,然后实现了摄像机固有参数的标定。此外,给出了相机和激光测距仪的内在和外在参数分离校准,并通过两个具有一定角度的交点校准板进行特征识别以提取融合字符。此外,提出了针对不同目标的外在参数估计的粒子群算法,并对初始粒子群算法采用了高斯消元法。仿真和实际实验结果表明,在21组实验中校准误差的标准偏差降低了10.175%,也证明了我们方法的准确性和有效性。

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