Robust feature detection using 2D wavelet transform under low light environment

Abstract

A novel local feature detection method is presented for mobile robot's visual simultaneous localization and map building (v-SLAM). Camera-based visual localization can handle complicated problems, such as kidnapping and shadowing, which come with other type of sensors. Fundamental requirement of robust self-localization is robust key-point extraction under affine transform and illumination change. Especially, localization under low light environment is crucial for the purpose of guidance and navigation. This paper presents an efficient local feature extraction method under low light environment. A more efficient local feature detector and a compensation scheme of noise due to the low contrast images are proposed. The propose scene recognition method is robust against scale, rotation, and noise in the local feature space. We adopt the framework of scale-invariant feature transform (SIFT), where the difference of Gaussian (DoG)-based scale-invariant feature detection module is replaced by the difference of wavelet (DoW).