基于测量误差不确定性加权的快速相机位姿估计算法

在相机位姿估计任务中,参考点位置的测量容易受测量仪器或者所处环境影响,出现一定程度的测量误差,其中误差较大的点对最终求解精度的影响很大,被称为野值点。文章针对现有算法抗野值点能力较弱的问题,提出一种基于测量误差不确定性加权的快速相机位姿估计算法。该算法以经典的正交迭代算法为基础,引入M估计方法,对目标函数根据参考点的测量误差赋予相应大小的权值,最大程度地将野值点的影响排除,并引入Kronecker积对计算过程进行优化,减少了迭代过程中的冗余计算,提高运算速度。仿真实验结果表明,改进后的算法提升了原算法的抗野值点能力,且计算速度更快。

Fast Camera Pose Estimation Algorithm Based on Measurement Error Uncertainty Weighting

In the task of camera pose estimation, the measurement of the position of the reference point is easily affected by the measuring instrument or the environment, leading a certain degree of measurement error. And the larger error point has a great influence on the final solution accuracy, which is called the outliers. Aiming at the problem that the existing algorithms have weak ability to resist outliers, a fast camera pose estimation algorithm based on measurement error uncertainty weighting is proposed. Based on the classic orthogonal iterative algorithm, the algorithm introduces Mestimation method, assigns the corresponding weight to the objective function according to the measurement error of the reference point, eliminates the influence of the outlier point to the greatest extent, and introduces the Kronecker product to optimize the calculation process. Thus the redundant calculations in the iterative process are reduced and the calculation speed is increased. The simulation results show that the improved algorithm not only improves the anti-outliers ability of the original algorithm, but also realizes faster calculation speed.