Pan Xiao, Liu Zhen
Opt Express. 2019 Feb 18;27(4):4364-4385. doi: 10.1364/OE.27.004364.
The structured-light vision sensor calibration approach based on planar target fails to generate reliable and accurate results due to the inaccurate localization of feature points in outdoor complex lighting environments. To address this issue, a novel high-accuracy calibration method that corrects image deviation is proposed for line-structured light vision sensor in this paper. The mathematical solution for stripe point location uncertainty is put forward and the location uncertainty of target feature points and stripe points is established. Moreover, the location deviation of all points is computed through large-scale nonlinear multi-step optimization based on the constraints of uncertainty. After compensation, the planar target based calibration method is adopted to solve the light plane equation. Both simulative and physical experiments have carried out to evaluate the performance of the proposed method and the results show that the proposed method is robust under large feature points localization deviation and can achieve the same measurement accuracy as the planar target calibration method under ideal imaging conditions, which reduces the requirements of the calibration environment, and has important practical engineering application value.
基于平面靶标的结构光视觉传感器标定方法，在室外复杂光照环境下，由于特征点定位不准确，无法生成可靠且准确的结果。针对这一问题，本文提出了一种用于线结构光视觉传感器的、校正图像偏差的新型高精度标定方法。提出了条纹点位置不确定性的数学解法，建立了目标特征点和条纹点的位置不确定性。此外，基于不确定性约束，通过大规模非线性多步优化计算所有点的位置偏差。补偿后，采用基于平面靶标的标定方法求解光平面方程。进行了仿真实验和物理实验来评估所提方法的性能，结果表明，所提方法在特征点定位偏差较大的情况下具有鲁棒性，在理想成像条件下能够达到与平面靶标标定方法相同的测量精度，降低了对标定环境的要求，具有重要的实际工程应用价值。
