Self-localization on texture statistics

The ability to localize ourselves in the outdoor world based on visual input even in absence of prior positional information is an important skill of our daily lives that comes naturally to us. However, the underlying mechanisms of this ability are poorly understood. Here, we show how simple texture statistics can be sufficient to provide a strong prior for the self-localization tasks. We find that statistics of common outdoor features such as tree density, foliage type or road structure provide a stronger cue for self-localization than the matching and recognition of less common landmarks such as lamp posts. We encourage the use of such common feature vectors as priors for self-localization systems and hypothesize that humans may use similar priors to assess the location from an unknown image.