A real-time distributed light field camera
EGRW '02 Proceedings of the 13th Eurographics workshop on Rendering
Using Real-Time Stereo Vision for Mobile Robot Navigation
Autonomous Robots
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Calculating Dense Disparity Maps from Color Stereo Images, an Efficient Implementation
International Journal of Computer Vision
A Cooperative Algorithm for Stereo Matching and Occlusion Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo Vision-Based Navigation in Unknown Indoor Environment
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Real-Time Consensus-Based Scene Reconstruction Using Commodity Graphics Hardware
PG '02 Proceedings of the 10th Pacific Conference on Computer Graphics and Applications
Fast correlation-based stereo matching with the reduction of systematic errors
Pattern Recognition Letters
SETN '08 Proceedings of the 5th Hellenic conference on Artificial Intelligence: Theories, Models and Applications
Autonomous vision-based robotic exploration and mapping using hybrid maps and particle filters
Image and Vision Computing
3D path planning with novel multiple 2D layered approach for complex human-robot interaction
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
Obstacle-Free Pathway Detection by Means of Depth Maps
Journal of Intelligent and Robotic Systems
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Autonomous navigation behaviors in robotics often require reliable depth maps. The use of vision sensors is the most popular choice in such tasks. On the other hand, accurate vision-based depth computing methods suffer from long execution times. This paper proposes a novel quad-camera based system able to calculate fast and accurately a single depth map of a scenery. The four cameras are placed on the corners of a square. Thus, three, differently oriented, stereo pairs result when considering a single reference image (namely an horizontal, a vertical and a diagonal pair). The proposed system utilizes a custom tailored, simple, rapidly executed stereo correspondence algorithm applied to each stereo pair. This way, the computational load is kept within reasonable limits. A reliability measure is used in order to validate each point of the resulting disparity maps. Finally, the three disparity maps are fused together according to their reliabilities. The maximum reliability is chosen for every pixel. The final output of the proposed system is a highly reliable depth map which can be used for higher level robotic behaviors.