Efficient visual loop closure detection in different times of day

Performing reliable and computationally efficient loop closure detection in real-world environments still remains a challenging problem. In this paper, we propose a novel method for efficient loop closure detection in different times of day. An illumination invariant color transform is applied to images that are represented by a whole-image descriptor, named PALM. The efficiency of our method resides either in description of the places or in image matching in which FLANN is used for fast nearest neighbor search. With this approach, searching time is decreased about 70 times compared to standard brute-force search with no significant loss of accuracy. According to the experiments that are performed in real-world datasets, the proposed method successfully accomplishes to detect loops under varied illumination conditions with high accuracy, and it allows real-time operation for long-life localization and mapping.