基于软分配 SIFT 特征的回环检测算法.

Loop closure detection can eliminate the accumulated error of visual SLAM, which is of great significance to the SLAM system. Among them, the widely used visual bag-of-word model algorithm has the problem of visual word identity and ambiguity, which affects the loop closure detection effect. To improve these problems and loop back detection effect, this paper proposed a loop closure detection algorithm based on soft-assigned SIFT features. The algorithm allocated the SIFT feature points extracted from the image to several words with the closest Euclidean distance, according to the distance sorting and weighting, and removed the feature points that were far away from the words to generate more discriminating descriptors. In addition, when filtering candidates, the algorithm added the proportion constraint of the same word feature points and the stability constraint of word shift to filter out a few candidates. In the experimental results, the algorithm compared with the traditional visual bag-of-words model and several loop closure detection algorithms in recent years, the recall rate at 100% accuracy rate improved under three data sets, and the average image query time was about 40 ms. The results show that the algorithm can improve the loop detection effect and ensure the real-time performance. [ABSTRACT FROM AUTHOR]