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首页> 外文会议>IAAI-12;Innovative applications of artificial intelligence conference;AAAI conference on artificial intelligence;Symposium on educational advances in artificial intelligence;AAAI-12;EAAI-12 >Automatic Targetless Extrinsic Calibration of a 3D Lidar and Camera by Maximizing Mutual Information
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Automatic Targetless Extrinsic Calibration of a 3D Lidar and Camera by Maximizing Mutual Information

机译：通过最大化相互信息对3D激光雷达和相机进行自动无目标外部校准

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摘要

This paper reports on a mutual information (MI) based algorithm for automatic extrinsic calibration of a 3D laser scanner and optical camera system. By using MI as the registration criterion, our method is able to work in situ without the need for any specific calibration targets, which makes it practical for in-field calibration. The calibration parameters are estimated by maximizing the mutual information obtained between the sensor-measured surface intensities. We calculate the Cramer-Rao-Lower-Bound (CRLB) and show that the sample variance of the estimated parameters empirically approaches the CRLB for a sufficient number of views. Furthermore, we compare the calibration results to independent ground-truth and observe that the mean error also empirically approaches to zero as the number of views are increased. This indicates that the proposed algorithm, in the limiting case, calculates a minimum variance unbiased (MVUB) estimate of the calibration parameters. Experimental results are presented for data collected by a vehicle mounted with a 3D laser scanner and an omnidirectional camera system.
机译：本文报告了一种基于互信息（MI）的算法，用于3D激光扫描仪和光学相机系统的自动外部校准。通过使用MI作为注册标准，我们的方法无需任何特定的校准目标就可以在原位工作，这使其在现场校准中变得可行。通过最大化在传感器测量的表面强度之间获得的互信息来估算校准参数。我们计算了Cramer-Rao-Lower-Bound（CRLB），并表明对于足够多的视图，估计参数的样本方差经验性地接近CRLB。此外，我们将校准结果与独立的地面真相进行比较，并观察到，随着视野数量的增加，平均误差在经验上也接近于零。这表明所提出的算法在极限情况下会计算校准参数的最小方差无偏（MVUB）估计。给出了针对安装有3D激光扫描仪和全向摄像头系统的车辆收集的数据的实验结果。

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