Algorithmic Fusion for More Robust Feature Tracking
International Journal of Computer Vision
Sensor and Information Fusion for Improved Vision-Based Vehicle Guidance
IEEE Intelligent Systems
Advances in Robust Multimodal Interface Design
IEEE Computer Graphics and Applications
Democratic Integration: Self-Organized Integration of Adaptive Cues
Neural Computation
Problems and solutions for anchoring in multi-robot applications
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Marco Somalvico Memorial Issue
Figure-ground separation by cue integration
Neural Computation
Constructing dependable certainty grids from unreliable sensor data
Robotics and Autonomous Systems
Affective cognitive modeling for autonomous agents based on Scherer's emotion theory
KI'06 Proceedings of the 29th annual German conference on Artificial intelligence
Machine perception in automation: a call to arms
EURASIP Journal on Embedded Systems - Special issue on networked embedded systems for energy management and buildings
Context representation and fusion via likelihood masks for target tracking
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
The evolution of representation in simple cognitive networks
Neural Computation
Hi-index | 0.00 |
This paper reviews the literature from the biological and cognitive sciences in sensory integration and derives principles for use in constructing intelligent sensor fusion systems. In particular, it presents psychophysical and neurophysical studies on how sensor fusion is accomplished and cognitive models of associated activities, including optimization of sensing configurations, improvement of sensing quality, and filtering of noise. The sensor fusion effects architecture for robot navigation is also presented as one example of how these insights from the biological and computer science can be applied to robotic sensor fusion. Experimental results demonstrates the utility of the biological and cognitive insights, especially that of fusion modes. Other representative architectures for robotic sensor fusion are contrasted with the biological and cognitive principles