Computational visual attention systems and their cognitive foundations: A survey
ACM Transactions on Applied Perception (TAP)
Using Local Symmetry for Landmark Selection
ICVS '09 Proceedings of the 7th International Conference on Computer Vision Systems: Computer Vision Systems
Using symmetrical regions of interest to improve visual SLAM
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Efficient neural models for visual attention
ICCVG'10 Proceedings of the 2010 international conference on Computer vision and graphics: Part I
Robotics and Autonomous Systems
Active vision in robotic systems: A survey of recent developments
International Journal of Robotics Research
An algorithm of determining the plane based on monocular vision and laser loop
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
Image retrieval by content based on a visual attention model and genetic algorithms
SBIA'12 Proceedings of the 21st Brazilian conference on Advances in Artificial Intelligence
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This paper is centered around landmark detection, tracking, and matching for visual simultaneous localization and mapping using a monocular vision system with active gaze control. We present a system that specializes in creating and maintaining a sparse set of landmarks based on a biologically motivated feature-selection strategy. A visual attention system detects salient features that are highly discriminative and ideal candidates for visual landmarks that are easy to redetect. Features are tracked over several frames to determine stable landmarks and to estimate their 3-D position in the environment. Matching of current landmarks to database entries enables loop closing. Active gaze control allows us to overcome some of the limitations of using a monocular vision system with a relatively small field of view. It supports 1) the tracking of landmarks that enable a better pose estimation, 2) the exploration of regions without landmarks to obtain a better distribution of landmarks in the environment, and 3) the active redetection of landmarks to enable loop closing in situations in which a fixed camera fails to close the loop. Several real-world experiments show that accurate pose estimation is obtained with the presented system and that active camera control outperforms the passive approach.