基于扩展正交迭代的快速加权的相机位姿估计

Accelerative and Weighted Camera Pose Estimation Based on Extended Orthogonal Iterative Algorithm

相机位姿估计算法多基于参考点而较少利用图像中的直线信息,本文对于相机位姿估计算法的抗干扰性和实时性,在扩展正交迭代的基础上,提出了一种基于点和直线段结合的快速加权的相机位姿估计算法,该算法以加权共线性误差和加权共面性误差之和为误差函数,根据计算初值的深度信息和重投影误差确定权重系数,并对整体进行加速优化,将每次迭代计算的时间复杂度从O(n)降到了O(1)。仿真实验结果表明算法可以抑制异常点的干扰,减少计算时间,旋转矩阵计算误差比传统正交迭代算法减少48.31%,平移向量计算误差减少48.79%,加速优化后的计算时间为加速前的47.11%。实物实验表明该算法可以充分利用检测到的参考点和参考直线信息,提高计算精度,有较高的实际应用价值。

The camera pose estimation algorithm is typically more based on reference points and less on the linear information in the image.In this paper,based on the extended orthogonal iteration,an accelerative and weighted camera pose estimation algorithm based on the combination of point and line segments is proposed to ensure a good anti-interference and real-time performance of the algorithm.The algorithm considers the sum of the weighted collinearity error and the weighted coplanarity error as the error function.The weight coefficients are determined by the depth information and the re-projection error of the initial value.In addition,the algorithm accelerates the iterative process and reduces the time complexity of the iteration process dramatically from O(n)to O(1).The simulation results demonstrate that the algorithm can suppress the interference of abnormal points and reduce the calculation time.The calculation accuracy of the rotation matrix is 51.69%higher than that of the traditional orthogonal iterative algorithm,and the calculation accuracy of the translation vector is increased by 51.21%.Furthermore,the calculation time after the acceleration is reduced by 47.11%.The physical experiment shows that the algorithm can make full use of the detected reference points and reference line information,improve the calculation accuracy,reduce the cumulative error,and has high practical application value.