Abstract-In this paper, we propose a visual place recognition algorithm which uses only straight line features in challenging outdoor environments. Compared to point features used in most existing place recognition methods, line features are easily found in man-made environments and more robust to environmental changes such as illumination, viewing direction, or occlusion. Candidate matches are found using a vocabulary tree and their geometric consistency is verified by a motion estimation algorithm using line segments. The proposed algorithm operates in real-time, and it is tested with a challenging real-world dataset with more than 10,000 database images acquired in urban driving scenarios.
In this paper, we present a monocular SLAM method which uses vertical and the floor line features as sensory input. A line-based partial 3D map was built, in which many structural properties of the environment can be included. The vertical line and the floor line can be represented as simple 2D objects in the floor plane. These two heterogeneous types of line features were developed as independent SLAM features and combined to represent the environment in a more complete fashion. Although the vertical and floor line use different parameterization and initialization methods, their measurement models are integrated into a unified EKF framework. Experimental results show that this method is practical in a structured indoor environment.
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