| 基于机器视觉的最大似然位姿估计算法 |
| |
| 引用本文： | 屈也频,张超然,吕余海.基于机器视觉的最大似然位姿估计算法[J].应用光学,2019,40(2):253-258. |
| |
| 作者姓名： | 屈也频 张超然 吕余海 |
| |
| 作者单位： | 海军研究院，上海 200235 |
| |
| 基金项目： | 国防预研基金项目4010905010301 |
| |
| 摘 要： | 针对现有位姿估计算法对采样数据不做任何的统计假设，缺少评判标准等问题，从信号的概率密度函数出发，推导了基于机器视觉的最大似然位姿估计的一般形式，并证明利用单幅图像时，在各向同性高斯噪声情况下传统迭代算法与最大似然估计等效。推导了位姿估计的克拉美-罗界，给出了位姿估计的方差下限。根据仿真结果可以看出，利用10张图像时，最大似然算法在噪声功率大于5dB的情况下，性能明显优于传统迭代算法，证明适当增加图像数可有效提高估计性能。
|
| 关 键 词： | 机器视觉 位姿估计 最大似然估计 克拉美-罗界 |
| 收稿时间： | 2018-07-24 |
Maximum likelihood pose estimation using machine vision |
| |
| Affiliation: | Naval Academy, Shanghai 200235, China |
| |
| Abstract: | The existing pose estimation algorithms do not make any statistical assumptions on the sampled data, and lack the evaluation criteria. Aiming at this problem, based on the probability density function of the signal, we derived the general form of maximum likelihood pose estimation based on machine vision was and proved that the traditional iterative algorithm is equivalent to the maximum likelihood estimation using single imag ein the case of isotropic Gaussian noise.What's more, we derived the Cramér-Rao bound of pose estimation, which could be regarded as the variance low bound of any unbiased estimations. By the analysis of the simulation, the maximum likelihood method is much better than the traditional iterative method by using 10 pictures when noise power is greater than 5 dB, it proves that the performance of pose estimation can be improved by increasing the number of images. |
| |
| Keywords: | |
| 本文献已被 CNKI 等数据库收录！ |
| 点击此处可从《应用光学》浏览原始摘要信息 |
|
点击此处可从《应用光学》下载全文 |
|
