Algorithm 772: STRIPACK: Delaunay triangulation and Voronoi diagram on the surface of a sphere
ACM Transactions on Mathematical Software (TOMS)
Multiple view geometry in computer vision
Multiple view geometry in computer vision
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Real-Time Simultaneous Localisation and Mapping with a Single Camera
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Preemptive RANSAC for live structure and motion estimation
Machine Vision and Applications
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
MonoSLAM: Real-Time Single Camera SLAM
IEEE Transactions on Pattern Analysis and Machine Intelligence
Planar Features for Visual SLAM
KI '08 Proceedings of the 31st annual German conference on Advances in Artificial Intelligence
Parallel Tracking and Mapping for Small AR Workspaces
ISMAR '07 Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality
Real-time and robust monocular SLAM using predictive multi-resolution descriptors
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
Real-time model-based SLAM using line segments
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
Discovering Higher Level Structure in Visual SLAM
IEEE Transactions on Robotics
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This paper concerns the building of enhanced scene maps during real-time monocular SLAM. Specifically, we present a novel algorithm for detecting and estimating planar structure in a scene based on both geometric and appearance and information. We adopt a hypothesis testing framework, in which the validity of planar patches within a triangulation of the point based scene map are assessed against an appearance metric. A key contribution is that the metric incorporates the uncertainties available within the SLAM filter through the use of a test statistic assessing error distribution against predicted covariances, hence maintaining a coherent probabilistic formulation. Experimental results indicate that the approach is effective, having good detection and discrimination properties, and leading to convincing planar feature representations.