Visual place recognition using landmark distribution descriptors
This work addresses the problem of robust place recognition in varying viewing conditions for applications like robotics or autonomous navigation, but it is incremental as it builds directly on existing approaches.
The paper tackles visual place recognition by extending a prior method to include descriptors that encode the spatial distribution of landmarks, resulting in improved performance with an average precision of around 70% compared to 58% and 50% for baseline methods.
Recent work by Suenderhauf et al. [1] demonstrated improved visual place recognition using proposal regions coupled with features from convolutional neural networks (CNN) to match landmarks between views. In this work we extend the approach by introducing descriptors built from landmark features which also encode the spatial distribution of the landmarks within a view. Matching descriptors then enforces consistency of the relative positions of landmarks between views. This has a significant impact on performance. For example, in experiments on 10 image-pair datasets, each consisting of 200 urban locations with significant differences in viewing positions and conditions, we recorded average precision of around 70% (at 100% recall), compared with 58% obtained using whole image CNN features and 50% for the method in [1].