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Abstract: localization plays a vital role in the mobile robot navigation system and is a fundamental capability for autonomous movement. In an indoor environment, the currently mainstream localization scheme uses 2D laser Lidar to build an occupancy grid map with simultaneous localization and mapping (SLAM) technology; it then locates the robot based on the known grid map. However, such solutions only work effectively in those areas with salient geometrical features. For areas with repeated, symmetrical, or similar structures, such as a long corridor, the conventional particle filter method will fail. To solve this crucial problem, this paper presents a novel coarse-to-fine paradigm that uses visual features to assist mobile robot localization in a long corridor. Firstly, the mobile robot is remote-controlled to move from the starting position to the end along a middle line. In the process of moving, a grid map is built using the laser-based SLAM method. At the same time, a visual map consisting of special images which are keyframes is created according to a keyframe selection strategy. The keyframes are associated with the robot's poses through time stamps. Secondly, a moving strategy is proposed, based on the extracted range features of the laser scans, to decide on an initial rough position. This is vital for the mobile robot because it gives instructions on where the robot needs to move to adjust its pose. Thirdly, the mobile robot captures images in a proper perspective according to the moving strategy and matches them with the image map to get a coarse localization. Finally, an improved particle filtering method is presented to achieve fine localization. Experimental results show that our method is effective and robust for global localization. The localization success rate reached 98.8% while the average moving distance was only 0.31 meters. In addition, the method also works well when the mobile robot is kidnapped to another position in the corridor.