Appearance Based Extraction of Planar Structure in Monocular SLAM
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
In-Situ 3D Indoor Modeler with a Camera and Self-contained Sensors
VMR '09 Proceedings of the 3rd International Conference on Virtual and Mixed Reality: Held as Part of HCI International 2009
Foundations and Trends in Robotics
Monocular vision SLAM for indoor aerial vehicles
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Online scene modeling for interactive AR applications
ICEC'10 Proceedings of the 9th international conference on Entertainment computing
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Visual simultaneous localisation and map-building supported by structured landmarks
International Journal of Applied Mathematics and Computer Science
Visual SLAM Based on Rigid-Body 3D Landmarks
Journal of Intelligent and Robotic Systems
Impact of Landmark Parametrization on Monocular EKF-SLAM with Points and Lines
International Journal of Computer Vision
Fast vision-based scene modeling for augmented reality in unprepared man-made environments
Journal of Ambient Intelligence and Smart Environments - Design and Deployment of Intelligent Environments
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In this paper, we describe a novel method for discovering and incorporating higher level map structure in a real-time visual simultaneous localization and mapping (SLAM) system. Previous approaches use sparse maps populated by isolated features such as 3-D points or edgelets. Although this facilitates efficient localization, it yields very limited scene representation and ignores the inherent redundancy among features resulting from physical structure in the scene. In this paper, higher level structure, in the form of lines and surfaces, is discovered concurrently with SLAM operation, and then, incorporated into the map in a rigorous manner, attempting to maintain important cross-covariance information and allow consistent update of the feature parameters. This is achieved by using a bottom-up process, in which subsets of low-level features are ldquofolded inrdquo to a parameterization of an associated higher level feature, thus collapsing the state space as well as building structure into the map. We demonstrate and analyze the effects of the approach for the cases of line and plane discovery, both in simulation and within a real-time system operating with a handheld camera in an office environment.