A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Application of Lie Algebras to Visual Servoing
International Journal of Computer Vision - Special issue on image-based servoing
Structure and Motion from Line Segments in Multiple Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparison of Edge Detectors: A Methodology and Initial Study
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
An Objective Comparison Methodology of Edge Detection Algorithms Using a Structure from Motion Task
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Real-Time Simultaneous Localisation and Mapping with a Single Camera
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Fusing Points and Lines for High Performance Tracking
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Three 2D-warping schemes for visual robot navigation
Autonomous Robots
Visual simultaneous localisation and map-building supported by structured landmarks
International Journal of Applied Mathematics and Computer Science
Mobile robot map building from time-of-flight camera
Expert Systems with Applications: An International Journal
Corisco: Robust edgel-based orientation estimation for generic camera models
Image and Vision Computing
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While many visual simultaneous localization and mapping (SLAM) systems use point features as landmarks, few take advantage of the edge information in images. Those SLAM systems that do observe edge features do not consider edges with all degrees of freedom. Edges are difficult to use in vision SLAM because of selection, observation, initialization and data association challenges. A map that includes edge features, however, contains higher-order geometric information useful both during and after SLAM. We define a well-localized edge landmark and present an efficient algorithm for selecting such landmarks. Further, we describe how to initialize new landmarks, observe mapped landmarks in subsequent images, and address the data association challenges of edges. Our methods, implemented in a particle-filter SLAM system, operate at frame rate on live video sequences.