Geometric computation for machine vision
Geometric computation for machine vision
Obstacle Detection Based on Qualitative and Quantitative 3D Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Direct Obstacle Detection and Motion from Spatio-Temporal Derivatives
CAIP '95 Proceedings of the 6th International Conference on Computer Analysis of Images and Patterns
Fusion of Range and Visual Data for the Extraction of Scene Structure Information
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Map Learning and High-Speed Navigation in RHINO
Map Learning and High-Speed Navigation in RHINO
Robot Homing by Exploiting Panoramic Vision
Autonomous Robots
Vision based system for camera tracking in eye-in-hand configuration
Proceedings of the 7th International Conference on Frontiers of Information Technology
Robust Range Finder Through a Laser Pointer and a Webcam
Electronic Notes in Theoretical Computer Science (ENTCS)
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In this paper, a method for inferring scene structure information based on both laser and visual data is proposed. Common laser scanners employed in contemporary robotic systems provide accurate range measurements, but only in 2D slices of the environment. On the other hand, vision is capable of providing dense 3D information of the environment. The proposed fusion scheme combines the accuracy of laser sensors with the broad visual fields of cameras toward extracting accurate scene structure information. Data fusion is achieved by validating 3D structure assumptions formed according to 2D range scans of the environment, through the exploitation of visual information. The proposed methodology is applied to robot motion planning and collision avoidance tasks by using a suitably modified version of the vector field histogram algorithm. Experimental results confirm the effectiveness of the proposed methodology.