Simultaneous localization and mapping (SLAM) is a technique to simultaneously perform mapping of environments and localization of a camera in real-time. Most existing monocular vision based SLAM techniques use point features as landmarks. However, images of artificial environments with little texture often contain many line segments, whereas few point features can be localized in such a scene. We propose here a realtime line-based SLAM system, and a novel method for describing the features of line segments (LEHF:Line-based Eight-directional Histogram Feature) in order to establish correct 2D and 3D line correspondences (2D-3D correspondences). LEHF is a fast and efficient way of describing features of line segments, which are detected by the line segment detector (LSD) method. The line-based orthogonal iteration (LBOI) method and the RANSAC algorithm are applied for the camera pose estimation. We conducted an experiment in order to test our SLAM system in a desktop environment and to perform augmented reality (AR). Moreover our SLAM system was evaluated by synthetic data.
|Publication status||Published - 2012 Jan 1|
|Event||2012 23rd British Machine Vision Conference, BMVC 2012 - Guildford, Surrey, United Kingdom|
Duration: 2012 Sep 3 → 2012 Sep 7
|Other||2012 23rd British Machine Vision Conference, BMVC 2012|
|Period||12/9/3 → 12/9/7|
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition