Illumination invariant representation of natural images for visual place recognition
Illumination changes are a typical problem for many outdoor long term applications such as visual place recognition. Keypoints may fail to match between images taken at the same location but different times of the day. Although recently some methods are presented for creating shadow free image representations, all of them have the limitation in terms of dealing with night images and non Planckian source of lighting. In this paper we present a new method for creating illumination invariant image representation using a combination of two existing methods based on natural image statistics that address the issue of illumination invariance. Unlike previous attempts at solving the problem of illumination invariant representation, the proposed method does not assume the ideal narrow band color camera nor a calibration step for each environment. We evaluate our method on real datasets to establish its accuracy and efficiency. Experimental results show that our method outperforms competing methods for illumination invariant image representation.
2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)