Robust ConvNet Landmark-Based Visual Place Recognition by Optimizing Landmark Matching

Visual place recognition (VPR) is a fundamental but challenging problem that has not been solved completely for a long time, especially in a kaleidoscopic environment. Recent advanced works which exploit ConvNet landmarks as a representation of an image for the VPR have demonstrated promising performance under condition and viewpoint changes. In this paper, we propose an improved ConvNet landmark-based VPR with better robustness and higher matching efficiency by extending this method from two aspects. First, we introduce hashing to find global optimal landmark matches for each landmark in the query image to boost the quality of matched landmark pairs. Second, we apply the sequence search for finding the best matches basing on the temporal information attached in both query and reference images. The experiments which conducted on four challengeable benchmark datasets show that our approach significantly enhances the robustness of traditional ConvNet landmark-based VPR and outperforms the state-of-the-art ConvNet feature-based VPR named SeqCNNSLAM. Moreover, our method has higher computing efficiency than previous ConvNet landmark-based VPR.