Driving saccade to pursuit using image motion
International Journal of Computer Vision
Simultaneous Localization and Map-Building Using Active Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Real-Time Simultaneous Localisation and Mapping with a Single Camera
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Active Search for Real-Time Vision
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
MonoSLAM: Real-Time Single Camera SLAM
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vision-Based SLAM: Stereo and Monocular Approaches
International Journal of Computer Vision
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
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
Exactly Sparse Delayed-State Filters for View-Based SLAM
IEEE Transactions on Robotics
Inverse Depth Parametrization for Monocular SLAM
IEEE Transactions on Robotics
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In tracking and geometrical vision, there are usually priors available on the image locations of features of interest. In this paper, we use these priors dynamically to guide a feature by feature matching search. Much less image processing and lower overall computational cost can be expected for getting global matchings. First, the concept of dynamic sequential search (DSS) is presented. Then, the problem of determining an optimal search order for DSS is investigated, when the probabilistic distribution of the features can be described by a multivariate Gaussian model. Based upon the general formulas for sequentially updating the predicted positions of the features as well as their innovation covariance, the theoretic lower bound for the sum of the areas of the features' search-regions is derived, and the necessary and sufficient condition for the optimal search order to approach this lower bound is presented. After that, an algorithm for dynamically determining a suboptimal search order is presented, with a computational complexity of O(n3), which is two magnitudes lower than those of the state-of-the-art algorithms. The effectiveness of the proposed method is validated by both statistical simulation and real-world experiments with a monocular visual SLAM (simultaneous localization and mapping) system. The results verify that the performance of the proposed method is better than the state-of-the-art algorithms, with both fewer image processing operations and lower overall computational cost.