摘要
为提高景像匹配系统的匹配速度和对初始定位误差、噪声的鲁棒性,对实时图与基准图之间的灰度分布关系进行分析,建立实时图和基准图之间的景像匹配模型,给出了一种改进的最小二乘景像匹配算法.该算法在最小二乘目标函数中引入一个辅助约束项构成综合目标函数,辅助约束项隐含有对量测输入平滑性的约束,提高了匹配算法的稳定性,运用牛顿法推导出该算法的递推公式,该算法充分利用了综合目标函数的一阶、二阶导数信息,因此具有较快的收敛速度.仿真结果表明了算法的有效性.
In order to improve the matching speed and the robustness to initial positioning error and noise of the scene matching system, the scene matching model was set by analyzing the relationship of gray level between real-time image and referenced image. An improved least-squares scene matching algorithm was proposed. The generalized cost function in the algorithm was constructed by adding an auxiliary constraint term to the sum of the squared errors. The auxiliary constraint term involved the requirement for the smoothness of measurement input to improve the stability of the algorithm. The recursive equations of the algorithm were derived using Newton iterative algorithm without any simplification. By using the first order and second order derivative information of the generalized cost function, the algorithm had high convergence speed. Simulation results demonstrate the effectiveness of the algorithm.
出处
《北京航空航天大学学报》
EI
CAS
CSCD
北大核心
2005年第8期848-852,共5页
Journal of Beijing University of Aeronautics and Astronautics
基金
航空科学基金资助项目 (0 3D5 10 0 7)
关键词
景像匹配
鲁棒性
最小二乘
综合目标函数
scene matching
robustness
least-squares
generalized cost function
