Loop Closure Detection Algorithm Based on Greedy Strategy for Visual SLAM
- 信息技术－已发表论文 
动态环境与视觉混淆严重影响视觉闭环检测性能.基于贪心策略,提出了一种在线构建视觉词典的闭环检测算法.算法优先处理Surf描述与已有单词Surf描述欧式距离最大的特征点,改进特征点与单词Surf描述最近邻的约束条件,生成了表征性能强、量化误差小的视觉词典,算法具备实时性,并在动态环境图像集与视觉混淆多发生的图像集上,在确保100%,准确率的条件下,最大召回率分别提升了5%,与4%,.The performance of loop closure detection is seriously affected by dynamic objects and perceptual aliasing in the environment. Based on greedy strategy, a real-time loop closure detection approach using online visual dictionary is proposed. The process of dictionary construction gives priority to dealing with Surf feature that has the maximum Euclidean distance from the closest vocabulary word. A more discriminative and representative visual vocabulary is produced through adding constraint condition to the nearest neighbor distance. This visual vocabulary guarantees a small quantization error. The proposed approach meets real-time constraints. Experiments based on datasets from dynamic environments and visually repetitive environments demonstrated that the largest recall rate increased by 5% and 4% respectively at 100% precision.<br/> © 2017, Editorial Board of Journal of Tianjin University(Science and Technology). All right reserved.