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3D LIDAR-Camera Extrinsic Calibration Using an Arbitrary Trihedron

机译:使用任意三面体的3D LIDAR摄像机外部校准

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摘要

This paper presents a novel way to address the extrinsic calibration problem for a system composed of a 3D LIDAR and a camera. The relative transformation between the two sensors is calibrated via a nonlinear least squares (NLS) problem, which is formulated in terms of the geometric constraints associated with a trihedral object. Precise initial estimates of NLS are obtained by dividing it into two sub-problems that are solved individually. With the precise initializations, the calibration parameters are further refined by iteratively optimizing the NLS problem. The algorithm is validated on both simulated and real data, as well as a 3D reconstruction application. Moreover, since the trihedral target used for calibration can be either orthogonal or not, it is very often present in structured environments, making the calibration convenient.
机译:本文提出了一种解决由3D LIDAR和照相机组成的系统的外部校准问题的新颖方法。两个传感器之间的相对转换通过非线性最小二乘(NLS)问题进行校准,该问题是根据与三面体对象相关的几何约束条件制定的。通过将NLS分为两个单独解决的子问题,可以获得NLS的精确初始估计。通过精确的初始化,可以通过迭代优化NLS问题来进一步完善校准参数。该算法在模拟数据和真实数据以及3D重建应用程序中均得到了验证。此外,由于用于校准的三面体目标可以是正交的,也可以不是正交的,因此它经常出现在结构化环境中,从而方便了校准。

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