Visual servoing from robust direct color image registration
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Monocular vision SLAM for indoor aerial vehicles
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Real-time Quadrifocal Visual Odometry
International Journal of Robotics Research
International Journal of Computer Vision
Real-time spherical mosaicing using whole image alignment
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Visual object tracking by an evolutionary self-organizing neural network
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Evolutionary neural networks for practical applications
ICIRA'12 Proceedings of the 5th international conference on Intelligent Robotics and Applications - Volume Part III
Hybrid tracking and mosaicking for information augmentation in retinal surgery
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
BodyNets '13 Proceedings of the 8th International Conference on Body Area Networks
Live RGB-D camera tracking for television production studios
Journal of Visual Communication and Image Representation
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The majority of visual simultaneous localization and mapping (SLAM) approaches consider feature correspondences as an input to the joint process of estimating the camera pose and the scene structure. In this paper, we propose a new approach for simultaneously obtaining the correspondences, the camera pose, the scene structure, and the illumination changes, all directly using image intensities as observations. Exploitation of all possible image information leads to more accurate estimates and avoids the inherent difficulties of reliably associating features. We also show here that, in this case, structural constraints can be enforced within the procedure as well (instead of a posteriori), namely the cheirality, the rigidity, and those related to the lighting variations. We formulate the visual SLAM problem as a nonlinear image alignment task. The proposed parameters to perform this task are optimally computed by an efficient second-order approximation method for fast processing and avoidance of irrelevant minima. Furthermore, a new solution to the visual SLAM initialization problem is described whereby no assumptions are made about either the scene or the camera motion. Experimental results are provided for a variety of scenes, including urban and outdoor ones, under general camera motion and different types of perturbations.