Visual Place Recognition in Changing Environments with Time-Invariant Image Patch Descriptors

Feature descriptors for images are a mature area of study within computer vision, and as a result, researchers now have access to many attribute-invariant features (e.g. scale, shift, rotation). However, changes to environments caused by changes in time, ie. weather and season, still pose a serious problem for current image matching systems. As the use of detailed 3D maps and visual Simultaneous Localization and Mapping (SLAM) for robotics becomes more widespread, the ability to match image points across different weather conditions, illumination, seasons, and vegetation growth becomes a more important problem to solve. In this paper, we propose a method to learn a timeinvariant image patch descriptor that can reliably match regions in images across the large-scale scenery changes caused by different weather and seasons. We use Convolutional Neural Networks (CNNs) to learn representations of image patches and in particular train a Siamese network with pairs of (non-)matching patches to enforce descriptor (dis)similarity. We enforce this by (maximizing)minimizing the Euclidean distance between descriptors of (non-)matching patches during training. To improve representation generalization, we work with the seldomused, large-scale Archive of Many Outdoor Scenes (AMOS) dataset.