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针对在单目视觉目标位姿测量过程中,特征点提取出现离群点的情况,提出一种基于去除离群点策略的位姿测量方法(ORPE)。建立了以特征点误差极大极小为原则的最优化目标函数,通过确定特征点最大观测误差值边界,判定并去除离群点,由此可消除离群点误差对位姿测量的影响。仿真实验使用ORPE对1 m×1 m×1 m的立方体目标进行位姿测量,验证了算法的正确性;使用ORPE测量Boeing飞机模型的位姿,平均姿态角误差2.07°,平均位移误差1.6%。通过和最小二乘测姿法(LSPE)结果对比分析可得ORPE法误差小于LSPE法误差。表明ORPE能有效去除离群点,同时提高位姿测量精度。
Aiming at the problem that the outlier points are extracted during the measurement of the monocular vision pose, a position and orientation measurement method (ORPE) based on the outlier removal strategy is proposed. An optimization objective function based on the principle of minimum error of feature points is established. By determining the boundary of the maximum observed error of feature points, the outlier is determined and removed, which can eliminate the influence of outliers on pose measurement. The simulation experiment uses ORPE to measure the position and attitude of a 1 m × 1 m × 1 m cube to verify the correctness of the algorithm. Using ORPE to measure the pose of the Boeing model, the average attitude error is 2.07 ° and the average displacement error is 1.6% . Compared with the result of least square method (LSPE), the error of ORPE is less than the error of LSPE. ORPE can effectively remove outliers, while improving the accuracy of pose measurement.