This paper presents a calibration algorithm that does not require an artificial target object to precisely estimate a rigid‐body transformation between a camera and a light detection and ranging (LIDAR) sensor. The proposed algorithm estimates calibration parameters by minimizing a cost function that evaluates the edge alignment between two sensor measurements. In particular, the proposed cost function is constructed using a projection model‐based many‐to‐many correspondence of the edges to fully exploit measurements with different densities (dense photometry and sparse geometry). The alignment of the many‐to‐many correspondence is represented using the Gaussian mixture model (GMM) framework. Here, each component of the GMM, including weight, displacement, and standard deviation, is derived to suitably capture the intensity, location, and influential range of the edge measurements, respectively. The derived cost function is optimized by the gradient descent method with an analytical derivative. A coarse‐to‐fine scheme is also applied by gradually decreasing the standard deviation of the GMM to enhance the robustness of the algorithm. Extensive indoor and outdoor experiments validate the claim that the proposed GMM strategy improves the performance of the proposed algorithm. The experimental results also show that the proposed algorithm outperforms previous methods in terms of precision and accuracy by providing calibration parameters of standard deviations less than 0.6° and 2.1 cm with a reprojection error of 1.78 for a 2.1‐megapixel image (2,048 × 1,024) in the best case.

中文翻译：

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通过将边缘与高斯混合模型对齐来自动进行无目标相机– LIDAR校准

本文提出了一种校准算法，该算法不需要人造目标即可精确估计相机与光检测和测距（LIDAR）传感器之间的刚体转换。所提出的算法通过最小化评估两个传感器测量之间的边缘对齐的成本函数来估算校准参数。特别是，建议的成本函数是使用基于投影模型的多对多构建的边缘的对应关系，以充分利用具有不同密度的测量（密集光度法和稀疏几何形状）。使用高斯混合模型（GMM）框架表示多对多对应关系的对齐方式。在这里，GMM的每个组成部分（包括重量，位移和标准偏差）都可以得出，以分别捕获边缘测量的强度，位置和影响范围。导出的成本函数通过带有分析导数的梯度下降法进行优化。通过逐渐减小GMM的标准偏差来应用从粗到精的方案，以增强算法的鲁棒性。大量的室内和室外实验验证了所提出的GMM策略改善了所提出算法的性能的说法。