基于层次化可导航小世界网络改进的SeqSLAM算法

Improved SeqSLAM Using Hierarchical Navigable Small World Graphs

SeqSLAM是移动机器人领域广泛使用的一种视觉定位算法,它对光照等因素较鲁棒,但受视角变化影响较大。另外,SeqSLAM采用了蛮力搜索匹配的方式,在较大规模数据集中无法满足实时性要求。针对以上问题,对SeqSLAM算法做了两方面的改进:首先将图像表示为局部聚合描述子向量,提取图像特征;然后采用层次化可导航小世界网络算法搜索相似图像序列,具有更高的搜索效率。测试表明,改进的SeqSLAM算法可以获得更高的精确率和召回率,搜索时间显著降低。

SeqSLAM is a widely used loop closure detection algorithm in mobile robot and autonomous vehicle field.It could recognize revisited places by comparing sequences of images even under dramatic changes of season,illumination,and weather.However,SeqSLAM is vulnerable to viewpoint changes.In addition,SeqSLAM compares sequences of images by brute force method,which prevents its real-time application to large-scale image datasets.To address these problems,we first represent each image by a kind of low dimensional description—vector of locally aggregated descriptors(VLAD)which is robust to viewpoint changes,and then replace the brute force method by an approximate nearest neighbor search algorithm—hierarchical navigable small world graphs(HNSW).Tests on publicly available datasets show that,the improved SeqSLAM with VLAD and HNSW could obtain much better detection results in the respect of precision-recall evaluation and the search time is reduced by orders of magnitude.