摘要
针对采用多状态约束卡尔曼滤波(MSCKF)的视觉惯性里程计定位精度易受特征点匹配异常值影响问题,提出了1种基于描述符辅助光流跟踪匹配的数据关联方法。该方法采用金字塔LK光流对序列图像中特征点进行跟踪匹配,计算每一对匹配点的rBRIEF描述符,根据Hamming距离对描述符的相似度进行判断消除异常匹配点。在实验中从特征点匹配主观效果以及定位精度2个方面评估本文方法的有效性,结果表明:所提出方法能够有效滤除动态场景下图像特征匹配的异常值,使用该方法处理后的图像进行MSCKF运动解算,位置结果漂移率小于0.38%,相较于未剔除异常匹配值的MSCKF算法结果,改善了54.7%,单帧图像处理时间约为39 ms。
The positioning accuracy of visual inertial odometer using multi-state constrained Kalman filter(MSCKF)is easily affected by mismatching points.A data association method is proposed for mitigating these outliers in this study.First,pyramid Lucas-Kanade(LK)optical flow is used to track and match the features among the sequence images.Second,the rBRIEF descriptors of each pair of matching points are achieved.Third,the Hamming distances between two rBRIEF descriptors can be calculated.Furthermore,the similarity of these descriptors is then evaluated according to Hamming distance.Last,the matching points of low similarity are eliminated as outliers in the data processing.The performances of the proposed method is assessed by the effectiveness of matching and positioning accuracy of the feature point.The results indicate that the proposed method can eliminate mismatching points in dynamic image processing.The outlier-eliminated images are applied for the MSCKF motion estimation.The derived drift rate of positioning result is less than 0.38%and shows an improvement of 54.7%with no outlier-eliminated MSCKF algorithm.The single-frame image processing time is about 39 ms.
作者
夏华佳
章红平
陈德忠
李团
XIA Huajia;ZHANG Hongping;CHEN Dezhong;LI Tuan(GNSS Research Center,Wuhan University,Wuhan 430079,China)
出处
《交通信息与安全》
CSCD
北大核心
2021年第6期153-161,共9页
Journal of Transport Information and Safety
基金
国家重点研发计划项目(SQ2018YFE020091)
长江勘测规划设计研究院开放创新基金项目(CX2020K04)。
