Robust Feature Detection Using 2D Wavelet Transform Under Low Light Environment

Abstract

A novel local feature detection method is presented for mobile ro- bot’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 require- ment of robust self-localization is robust key-point extraction under affine transform and illumination change. Especially, localization under low light en- vironment is crucial for the purpose of guidance and navigation. This paper presents an efficient local feature extraction method under low light environ- ment. A more efficient local feature detector and a compensation scheme of noise due to the low contrast images are proposed. The propose scene recogni- tion 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).