基于各点异性理论的基础矩阵鲁棒估计

针对基础矩阵常用算法对噪声过于敏感、抗干扰能力差等缺点，基于误差与变量相关（Errors-in—Variables，EIV）模型提出1种新的鲁棒性基础矩阵估计算法．该算法采用各点异性回归技术，建立EIV模型，依据数据矢量观测集合最优地估计EIV模型参数和数据矢量真值集合．实验结果表明，在存在较大噪声干扰的条件下，此算法仍能较为准确地估计基础矩阵，具有良好的鲁棒性和较快的运算速度.

Robust estimation on fundamental matrix based on heteroscedastic theory

With the disadvantages of high sensitivity to noise and poor anti-jamming capability in many common fundamental matrix algorithms,a new algorithm is proposed based on EIV(Errors-in-Variables) model.In the algorithm,the heteroscedastic regression technique is adopted,the EIV model is established,and the optimal parameters of EIV model and truth-value set of data vectors are estimated according to the observation set of data vectors.The experiments show that the algorithm can estimate fundamental matrix robus...