Place Recognition with WxBS Retrieval

We present a novel visual place recognition method designed for operation in challenging conditions such as encountered in day to night or winter to summer matching. The proposed WxBS Retrieval method is novel in enriching a bag of words approach with the use of multiple detectors, descriptors with suitable visual vocabularies, view synthesis, and adaptive thresholding to compensate for large variations in contrast and richness of features in different conditions. The performance of the method evaluated on the public Visual Place Recognition in Changing Environments (VPRiCE) dataset was achieved with precision 0.689 and recall 0.798 and F1-score 0.740. The precision and F1 score are best results so far reported for VPRiCE dataset. Experiments show that the combination of retrieval and matching algorithms with detectors and descriptors insensitive to gradient reversal and contrast lead to both high accuracy and scalability.