Visual Localization Using Sequence Matching Based on Multi-feature Combination

Visual localization in changing environment is one of the most challenging topics in computer vision and robotic community. The difficulty of this task is related to the strong appearance changes that occur in scenes due to presence of dynamic objects, weather or season changes. In this paper, we propose a new method which operates by matching query image sequences to an image database acquired previously (video acquired when the vehicle was traveling the environment). In order to improve matching accuracy, multi-feature is constructed by combining global GIST descriptor and local LBP descriptor to represent image sequence. Then, similarity measurement according to Chi-square distance is used for effective sequences matching. For experimental evaluation, we conducted study of the relationship between image sequence length and sequences matching performance. To show its effectiveness, the proposed method is tested and evaluated in four seasons outdoor environments. The results have shown improved precision-recall performance against state-of-the-art SeqSLAM algorithm.