基于网格的运动统计特征配准算法在医疗服务机器人中的应用

目的 针对医疗服务机器人目标识别中特征匹配准确率低、实时性差等问题，提出一种基于评分框架的基于网格的运动统计（SF-GMS）特征配准算法。方法 SF-GMS算法使用网格对特征点邻域进行分割，统计每个邻域中特征点的数量，设置评分框架函数，根据邻域特征点数量和评分阈值判断特征匹配准确性。结果和结论 与典型性特征配准算法随机采样一致性（RANSAC）算法相比，SF-GMS算法能有效提高特征成功匹配率，并具有较好的实时性；对光照视角、遮挡、仿射、比例尺度缩放和旋转等环境变化具有较好的稳定性，能满足模拟医院病房场景下服务机器人自主导航的需求。
Objective To propose a scoring framework grid-based motion statistics (SF-GMS) feature matching algorithm to improve the poor real-time ability and inaccurate matching in the process of target recognition for medical service robots.Methods The feature point neighborhoods were segmented by SF-GMS algorithm using the grids, and the number of feature points in each neighborhood was counted and the scoring frame function was set to judge the feature matching accuracy according to the number of neighborhood feature points and the scoring threshold.Results and conclusion Compared with random sample consensus algorithm, SF-GMS algorithm effectively improved the successful matching rate, and had better real-time performance. SF-GMS algorithm had better stability to the changes of illumination view, occlusion, affine, scale and rotation, and could meet the demand of autonomous navigation in simulating hospital ward scenario for medical service robots