Adding Cues to Binary Feature Descriptors for Visual Place Recognition
This work addresses visual place recognition for robotics and computer vision applications, presenting an incremental improvement by augmenting existing binary descriptors with cues.
The paper tackles the problem of improving visual place recognition by embedding continuous and selector cues into binary feature descriptors, resulting in enhanced performance across five benchmark datasets and several state-of-the-art image retrieval approaches.
In this paper we propose an approach to embed continuous and selector cues in binary feature descriptors used for visual place recognition. The embedding is achieved by extending each feature descriptor with a binary string that encodes a cue and supports the Hamming distance metric. Augmenting the descriptors in such a way has the advantage of being transparent to the procedure used to compare them. We present two concrete applications of our methodology, demonstrating the two considered types of cues. In addition to that, we conducted on these applications a broad quantitative and comparative evaluation covering five benchmark datasets and several state-of-the-art image retrieval approaches in combination with various binary descriptor types.