采摘机器人果实识别的多源图像配准

摘要

In order to improve the accuracy rates and lower the impact on fruit recognition in unstructured environment, a combination of PMD camera and color camera was used to capture multi-source images of orchard scenes, SURF algorithm was used for extracting scale invariant features, Euclidean distance was regarded as a measure for judging the similarity of features, the ratio of distance from the closest neighbor to the distance of the second closest was utilized for initially matching feature vectors, BBF algorithm was devoted to speed up the closest neighbor' s query, a kind of iterative method between picking out outlier points and optimization of model was applied to purify results, the performance of image registration was evaluated according to the MSE, NMI and COEF. The different experimental results show that the amount of information locking to object are enriched by the combination of cameras, the hybrid algorithm is realtime, robust and has ideal precision, which meets the need of orchard test.%为了提高果实识别的准确性,减少非结构化环境对识别的影响,使用基于光学混合探测(PMD)技术的深度摄像机与RGB摄像机组合捕获果园环境的多源图像；SURF算法提取待配准图像的尺度不变特征,欧式距离作为判断特征相似性的测度,最近邻与次近邻比值实现特征向量的初匹配,最近邻的搜索策略加速匹配过程；剔除异常点与优化模型交替迭代的方法提纯匹配结果；并以均方误差(MSE)、归一化互信息(NMI)和相关系数(COEF)作为配准效果的客观评价标准.不同试验结果表明:双摄像机组合丰富了锁定目标区域的信息量,配准算法的实时性、鲁棒性及精度均能满足果园试验的要求.