SoF-SLAM: Segments-on-Floor-based monocular SLAM

In this paper, we propose a novel monocular SLAM method in corridor environment which employs Segments-on-Floor (SoF) as feature data. Given that the height of the camera and the angle between the camera and the floor are known, an image of the SoF can be efficiently distinguished from the other space-lines by a simple data-association method, deriving the line correspondence from a simplified homography matrix of two sequentially gathered images. Furthermore, use of SoF simplifies the analysis of the geometrical property of the camera projection matrix. Therefore, we can reconstruct SoF by using a one-step inverse projection. Once SoF is calculated from visual data processing, they are then used in a normal SLAM process as feature data. We employ a simple particle filter in our corridor SLAM. Experimental results show that it is sufficient for mapping a moderately sized building environment.