Visual Localization Based on Place Recognition Using Multi-feature Combination (D-lambda LBP plus plus HOG)

This paper presents a visual localization method based on multi-feature combination (D-lambda LBP++HOG) using stereo images. We integrate disparity information into complete local binary features (lambda LBP) to form a robust global image description (D-lambda LBP). In order to represent the scene in depth, multi-feature fusion of D-lambda LBP and HOG features, provides valuable information and permits to decrease the effect of some typical problems in place recognition such as perceptual aliasing. It improves visual recognition performance by taking the advantage of depth, texture and shape information. In addition, for real-time visual localization, local sensitive hashing method (LSH) is used to compress the high dimensional multi-feature into binary vectors. It can thus speed up the process of image matching. To show its effectiveness, the proposed method is tested and evaluated using real datasets acquired in outdoor environments. Given the obtained results, our approach allows more effective visual localization compared with the state-of-the-art method FAB-MAP.