Modeling visual attention via selective tuning
Artificial Intelligence - Special volume on computer vision
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data- and Model-Driven Gaze Control for an Active-Vision System
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simultaneous Localization and Map-Building Using Active Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mobile Robot Localisation Using Active Vision
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Object-based visual attention for computer vision
Artificial Intelligence
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Real-Time Simultaneous Localisation and Mapping with a Single Camera
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Robust Real-Time Face Detection
International Journal of Computer Vision
VOCUS: A Visual Attention System for Object Detection and Goal-Directed Search (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence)
Detecting Loop Closure with Scene Sequences
International Journal of Computer Vision
Goal-directed search with a top-down modulated computational attention system
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
A multilevel relaxation algorithm for simultaneous localization and mapping
IEEE Transactions on Robotics
Saliency-Based Obstacle Detection and Ground-Plane Estimation for Off-Road Vehicles
ICVS '09 Proceedings of the 7th International Conference on Computer Vision Systems: Computer Vision Systems
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Visual attention regions are useful for many applications in the field of computer vision and robotics. Here, we introduce an application to simultaneous robot localization and mapping. A biologically motivated attention system finds regions of interest which serve as visual landmarks for the robot. The regions are tracked and matched over consecutive frames to build stable landmarks and to estimate the 3D position of the landmarks in the environment. Matching of current landmarks to database entries enables loop closing and global localization. Additionally, the system is equipped with an active camera control, which supports the system with a tracking, a re-detection, and an exploration behaviour. We present experiments which show the applicability of the system in a real-world scenario. A comparison between the system operating in active and in passive mode shows the advantage of active camera control: we achieve a better distribution of landmarks as well as a faster and more reliable loop closing.