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首页> 外文期刊>Measurement Science & Technology >Calibration of laser scanner and camera fusion system for intelligent vehicles using Nelder-Mead optimization
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Calibration of laser scanner and camera fusion system for intelligent vehicles using Nelder-Mead optimization

机译:使用Nelder-Mead优化对智能车辆的激光扫描仪和照相机融合系统进行校准

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摘要

A novel method is presented here for the calibration of a sensor fusion system for intelligent vehicles. In this example, the sensors are a camera and a laser scanner which observe the same scene from different viewpoints. The method employs the Nelder-Mead direct search algorithm to minimize the sum of squared errors between the image coordinates and the re-projected laser data by iteratively adjusting and improving the calibration parameters. The method is applied to a real set of data collected from a test vehicle. Using only 11 well-spaced target points observable by each sensor, 12 intrinsic and extrinsic parameters indicating the position relationship between the sensors can be estimated to give an accurate projection. Experiments show that the method can project the laser points onto the image plane with an average error of 1.01 pixels (1.51 pixels worst case).
机译:这里提出了一种用于校准智能车辆传感器融合系统的新颖方法。在此示例中,传感器是照相机和激光扫描仪,它们从不同的角度观察同一场景。该方法采用Nelder-Mead直接搜索算法,通过迭代地调整和改进校准参数来最小化图像坐标和重新投影的激光数据之间的平方误差之和。该方法适用于从测试车辆收集的真实数据集。使用每个传感器仅可观察到的11个间隔良好的目标点,可以估算12个表示传感器之间位置关系的内在和外在参数,以给出准确的投影。实验表明,该方法可以将激光点投射到图像平面上,平均误差为1.01像素(最坏情况为1.51像素)。

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