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Scalable Extrinsic Calibration of Omni-Directional Image Networks
International Journal of Computer Vision
Match Propogation for Image-Based Modeling and Rendering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric Properties of Central Catadioptric Line Images
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
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OMNIVIS '00 Proceedings of the IEEE Workshop on Omnidirectional Vision
Omni-Directional Vision for Robot Navigation
OMNIVIS '00 Proceedings of the IEEE Workshop on Omnidirectional Vision
A General Approach for Egomotion Estimation with Omnidirectional Images
OMNIVIS '02 Proceedings of the Third Workshop on Omnidirectional Vision
Image Processing in Catadioptric Planes: Spatiotemporal Derivatives and Optical Flow Computation
OMNIVIS '02 Proceedings of the Third Workshop on Omnidirectional Vision
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ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Catadioptric Line Features Detection using Hough Transform
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structure from Motion with Wide Circular Field of View Cameras
IEEE Transactions on Pattern Analysis and Machine Intelligence
RANSAC for (Quasi-)Degenerate data (QDEGSAC)
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Effective and Generic Structure from Motion using Angular Error
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
A Fast Detector of Line Images Acquired by an Uncalibrated Paracatadioptric Camera
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
IEICE - Transactions on Information and Systems
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Computer Vision and Image Understanding
Relative Pose Calibration Between Visual and Inertial Sensors
International Journal of Robotics Research
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International Journal of Computer Vision
Automatic scene structure and camera motion using a catadioptric system
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Computer Vision and Image Understanding
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TAINN'05 Proceedings of the 14th Turkish conference on Artificial Intelligence and Neural Networks
Camera Models and Fundamental Concepts Used in Geometric Computer Vision
Foundations and Trends® in Computer Graphics and Vision
Rotation estimation and vanishing point extraction by omnidirectional vision in urban environment
International Journal of Robotics Research
Hypercatadioptric line images for 3D orientation and image rectification
Robotics and Autonomous Systems
Self-calibration of hybrid central catadioptric and perspective cameras
Computer Vision and Image Understanding
Short baseline line matching for central imaging systems
Pattern Recognition Letters
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Previous works have shown that catadioptric systems are particularly suited for egomotion estimation thanks to their large field of view and thus numerous algorithms have already been proposed in the literature to estimate the motion. In this paper, we present a method for estimating six degrees of freedom camera motions from central catadioptric images in man-made environments. State-of-the-art methods can obtain very impressive results. However, our proposed system provides two strong advantages over the existing methods: first, it can implicitly handle the difficulty of planar/non-planar scenes, and second, it is computationally much less expensive. The only assumption deals with the presence of parallel straight lines which is reasonable in a man-made environment. More precisely, we estimate the motion by decoupling the rotation and the translation. The rotation is computed by an efficient algorithm based on the detection of dominant bundles of parallel catadioptric lines and the translation is calculated from a robust 2-point algorithm. We also show that the line-based approach allows to estimate the absolute attitude (roll and pitch angles) at each frame, without error accumulation. The efficiency of our approach has been validated by experiments in both indoor and outdoor environments and also by comparison with other existing methods.