Leveraging image-based prior in cross-season place recognition

In this paper, we address the challenging problem of single-view cross-season place recognition. A new approach is proposed for compact discriminative scene descriptor that helps in coping with changes in appearance in the environment. We focus on a simple effective strategy that uses objects whose appearance remain the same across seasons as valid landmarks. Unlike popular bag-of-words (BoW) scene descriptors that rely on a library of vector quantized visual features, our descriptor is based on a library of raw image data (e.g., visual experience shared by colleague robots, publicly available photo collections from Google StreetView), and directly mines it to identify landmarks (i.e., image patches) that effectively explain an input query/database image. The discovered landmarks are then compactly described by their pose and shape (i.e., library image ID, and bounding boxes) and used as a compact discriminative scene descriptor for the input image. We collected a dataset of single-view images across seasons with annotated ground truth, and evaluated the effectiveness of our scene description framework by comparing its performance to that of previous BoW approaches, and by applying an advanced Naive Bayes Nearest neighbor (NBNN) image-to-class distance measure.