plays a vital role in the navigation system and is a fundamental capability for autonomous movement. In an indoor environment, the current mainstream scheme uses two-dimensional (2D) laser light detection and ranging (LiDAR) to build an occupancy grid map with simultaneous and mapping (SLAM) technology; it then locates the robot based on the known grid map. However, such solutions work effectively only in those areas with salient geometrical features. For areas with repeated, symmetrical, or similar structures, such as a long corridor, the conventional ing method will fail. To solve this crucial problem, this paper presents a novel coarse-to-fine paradigm that uses to assist in a long corridor. First, the is remote-controlled to move from the starting position to the end along a middle line. In the moving process, 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 timestamps. Second, 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 because it gives instructions on where the robot needs to move to adjust its pose. Third, the captures images in a proper perspective according to the moving strategy and matches them with the image map to achieve a coarse . Finally, an improved ing method is presented to achieve fine . Experimental results show that our method is effective and robust for global . The success rate reaches 98.8% while the average moving distance is only 0.31 m. In addition, the method works well when the is kidnapped to another position in the corridor.