为满足高速复杂流场中投放物位姿高精度测量的要求,分析了图像噪声对位姿估计误差的影响。在传统非线性优化位姿算法的基础上,提出了自适应重投影误差的单目位姿估计优化方法。该方法以位姿估计初值为中心设置了约束区间,建立新的罚函数将约束非线性优化转化为无约束非线性优化,分析像平面重投影误差与约束区间的关系并建立相应的数学模型,根据模型自动调节约束区间,基于自适应重投影误差对位姿估计参数进行约束非线性优化。仿真结果表明,该方法在不同大小图像噪声下的重投影误差和位姿估计参数都有最优解,且优于传统非线性优化算法,具有较高的位姿估计精度。

This study analyzes the effects of image noise on pose estimation error to meet the requirements of high-accuracy pose measurements in high-speed complex flow fields. A monocular pose estimation optimization algorithm of adaptive reprojection error is proposed based on the traditional nonlinear optimization pose algorithm. In the algorithm, the initial value of pose estimation is set as the center, wherein the constraint interval is set. A new penalty function is formulated to transform the constrained nonlinear optimization into an unconstrained nonlinear optimization. The relation between the image reprojection error and constraint interval is analyzed; then, the corresponding mathematical model is built based on the analysis results. The constraint interval is automatically adjusted according to the model to optimize the parameters of pose estimation for the constrained nonlinear adaptive reprojection error. The simulation results reveal that the proposed algorithm provides an optimal solution for the reprojection error and pose estimation parameters under different image noise levels. The proposed algorithm is superior to the traditional nonlinear optimization algorithm and has higher pose estimation accuracy.