Condition and Viewpoint Invariant Omni-Directional Place Recognition Using CNN

Robust place recognition systems are essential for long term localization and autonomy. Such systems should recognize scenes with both conditional and viewpoint changes. In this paper, we present a deep learning based planar omni-directional place recognition approach that can simultaneously cope with conditional and viewpoint variations, including large viewpoint changes, which current methods do not address. We evaluate the proposed method on two real world datasets dealing with illumination, seasonal/weather changes and changes occurred in the environment across a period of 1 year, respectively. We provide both quantitative (recall at 100% precision) and qualitative (confusion matrices) comparison of the basic pipeline of place recognition for the omni-directional approach with single-view and side-view camera approaches. The results prove the efficacy of the proposed omnidirectional deep learning method over the single-view and side-view cameras in dealing with both conditional and large viewpoint changes.