Review and analysis of solutions of the three point perspective pose estimation problem
International Journal of Computer Vision
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
MonoSLAM: Real-Time Single Camera SLAM
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scan registration for autonomous mining vehicles using 3D-NDT: Research Articles
Journal of Field Robotics - Special Issue on Mining Robotics
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Journal of Field Robotics - Three-Dimensional Mapping, Part 3
1-point RANSAC for EKF-based structure from motion
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Fast registration based on noisy planes with unknown correspondences for 3-D mapping
IEEE Transactions on Robotics
A fast probabilistic model for hypothesis rejection in SIFT-Based object recognition
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Guest Editorial Special Issue on Visual SLAM
IEEE Transactions on Robotics
Inverse Depth Parametrization for Monocular SLAM
IEEE Transactions on Robotics
Discovering Higher Level Structure in Visual SLAM
IEEE Transactions on Robotics
Map-based navigation in mobile robots
Cognitive Systems Research
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In current visual SLAM methods, point-like landmarks (As in Filliat and Meyer (Cogn Syst Res 4(4):243---282, 2003), we use this expression to denote a landmark generated by a point or an object considered as punctual.) are used for representation on maps. As the observation of each point-like landmark gives only angular information about a bearing camera, a covariance matrix between point-like landmarks must be estimated in order to converge with a global scale estimation. However, as the computational complexity of covariance matrices scales in a quadratic way with the number of landmarks, the maximum number of landmarks that is possible to use is normally limited to a few hundred. In this paper, a visual SLAM system based on the use of what are called rigid-body 3D landmarks is proposed. A rigid-body 3D landmark represents the 6D pose of a rigid body in space (position and orientation), and its observation gives full-pose information about a bearing camera. Each rigid-body 3D landmark is created from a set of N point-like landmarks by collapsing 3N state components into seven state components plus a set of parameters that describe the shape of the landmark. Rigid-body 3D landmarks are represented and estimated using so-called point-quaternions, which are introduced here. By using rigid-body 3D landmarks, the computational time of an EKF-SLAM system can be reduced up to 5.5%, as the number of landmarks increases. The proposed visual SLAM system is validated in simulated and real video sequences (outdoor). The proposed methodology can be extended to any SLAM system based on the use of point-like landmarks, including those generated by laser measurement.