摘要
提出了一种基于改进TF-IDF的视觉SLAM回环检测算法,用于检测曾经访问过的位置,来消除定位过程中的累积误差.首先,针对在人造建筑场景中使用SLAM算法对图像点特征进行计算容易导致检测失败的问题,采用图像中的直线作为特征来进行回环检测的计算.其次,在LBD(line band descriptor)图像线特征描述子的基础上进一步提取了二进制LBD描述子来进行视觉词典的构建,保证了线特征的处理效率.提出了一种改进的TF-IDF(term frequency&inverse document frequency)单词权重确定方法,提高了视觉单词评分之间的区分度.最后,以室内建筑环境和输电线路场景为例进行实验,结果显示,所提出的基于线特征的回环检测算法比基于点特征的算法有较高的检测准确率,有助于提高SLAM算法的计算性能.
A term frequency & inverse document frequency(TF-IDF) based loop closure detection algorithm for vision-based simultaneous localization and mapping(SLAM) was presented. The algorithm could detect the revisited places of the robot to eliminate the accumulated error during localization. Firstly, line features were used to detect the loop closure in the man-made environment, which is due to the point features could lead to a failure of SLAM. Secondly, the binary line band descriptor(LBD) was constructed based on LBD to build the vision dictionary, improving the efficiency of processing line features. Thirdly, a variant of TF-IDF was proposed to enhance the word discrimination. Finally, the experiments were carried out on indoor scene and transmission line scene.Experimental results show that the line-based algorithm outperforms point-based algorithm on the detection rate. It can help in ensuring the proformance of the SLAM computation.
作者
董蕊芳
柳长安
杨国田
Dong Ruifang;Liu Chang’an;Yang Guotian(School of Control and Computer Engineering,North China Electric Power University,Beijing 102206,China)
出处
《东南大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2019年第2期251-258,共8页
Journal of Southeast University：Natural Science Edition
基金
国家自然科学基金资助项目(61105083)
中央高校基本科研业务费专项资金资助项目(2018ZD06)
关键词
SLAM
回环检测
人造建筑场景
二进制LBD
改进的TF-IDF方法
simultaneous localization and mapping(SLAM)
loop closure detection
man-made environment
binary line band descriptor(LBD)
term frequency&inverse document frequency(TF-IDF)method
