基于特征地图的改进回环检测算法

Improved Loop Detection Algorithm Based on Feature Map

为提高移动机器人回环检测模块的准确率和实时性,提出一种基于特征地图的改进回环检测算法。在传统模型的基础上,对关键帧的特征点进行筛选,选取高效特征点构建特征地图,利用视觉字典树对特征地图和关键帧进行场景描述;对词袋模型进行改进,将场景分割用在图像信息的提取和特征聚类上;建立基于分层K++均值的视觉字典树,得到改进的基于分层金字塔TF-IDF(term frequency inverse document frequency)的匹配方法。实验结果证明:相比FAB-MAP(fast appearance-based mapping)和RGB-D SLAM v2,改进算法在特征点规模、实时性、召回率方面性能更优。

In order to improve the accuracy and real-time of the loop detection module of mobile robot,an improved loop detection algorithm based on feature map is proposed.On the basis of traditional model,selecting the feature points of key frames and efficient feature points to build feature maps,using visual dictionary tree to description scene of feature map and key frame.Secondly,to improve the bag of words model,scene segmentation is applied to image information extraction and feature clustering.Finally,a visual dictionary tree based on hierarchical K++means is established,and an improved method of score matching based on hierarchical pyramid TF-IDF(term frequency-inverse document frequency)is obtained.The test results show that compared with FAB-MAP(fast appearance-based mapping)and RGB-D SLAM v2,the improved algorithm has better performance in feature point size,real-time performance and recall rate.