Visual Localization by Place Recognition Based on Multifeature (D-lambda LBP plus plus HOG)

Visual localization is widely used in the autonomous navigation system and Advanced Driver Assistance Systems (ADAS). This paper presents a visual localization method based on multifeature fusion and disparity information using stereo images. We integrate disparity information into complete center-symmetric local binary patterns (CSLBP) to obtain a robust global image description (D-CSLBP). In order to represent the scene in depth, multifeature fusion of D-CSLBP and HOG features provides valuable information and permits decreasing the effect of some typical problems in place recognition such as perceptual aliasing. It improves visual recognition performance by taking advantage of depth, texture, and shape information. In addition, for real-time visual localization, local sensitive hashing method (LSH) was used to compress the high-dimensional multifeature 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.