Object Tracking with a Multiagent Robot System and a Stereo Vision Camera
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TLDR
In this paper, a working multiagent robot application that can be used for tracking, tooling or handling operations with the use of stereo vision in unstructured laboratory environment is described.About:
This article is published in Procedia Engineering.The article was published on 2014-01-01 and is currently open access. It has received 34 citations till now. The article focuses on the topics: Mobile robot navigation & Robot calibration.read more
Citations
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Calibration and accuracy analysis of robotic belt grinding system using the ruby probe and criteria sphere
TL;DR: An improved method is proposed in this paper to calibrate the tool (grinding machine) frame and workpiece (aero-engine blade) frame by holding the ruby probe as the main calibration tool to enhance the accuracy of robotic calibration system.
Journal ArticleDOI
Teaching robots to do object assembly using multi-modal 3D vision
TL;DR: In this article, the authors developed an intelligent robot assembly system using multi-modal vision for next-generation industrial assembly, which includes two phases where in the first phase human beings demonstrate assembly to robots and in the second phase robots detect objects, plan grasps and assemble objects following human demonstration using AI searching.
Journal ArticleDOI
Development and Experimental Evaluation of a 3D Vision System for Grinding Robot.
TL;DR: A 3D vision system mounted on the robot’s fourth joint, which is used to detect the machining target of the grinding robot, and could easily integrate into an intelligent grinding system and may be suitable for industrial sites.
Proceedings ArticleDOI
Medical applicability of a low-cost industrial robot arm guided with an optical tracking system
TL;DR: To measure and assess medical applicability of a low-cost, lightweight industrial robot arm (Universal robot UR5) guided with the medically certified optical tracking system (Polaris Vicra) to positions registered from a CT scan.
Journal ArticleDOI
An FPGA stereo matching unit based on fuzzy logic
M. Pérez-Patricio,Abiel Aguilar-González,Miguel Arias-Estrada,H.R. Hernández-de León,J.L. Camas-Anzueto,J. A. de Jesús Osuna-Coutiño +5 more
TL;DR: An FPGA stereo matching unit based on fuzzy logic outperforms the accuracy of most of real-time stereo matching algorithms in the state of the art and increases the processing speed.
References
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Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Journal ArticleDOI
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography
TL;DR: New results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form that provide the basis for an automatic system that can solve the Location Determination Problem under difficult viewing.
Journal ArticleDOI
Speeded-Up Robust Features (SURF)
TL;DR: A novel scale- and rotation-invariant detector and descriptor, coined SURF (Speeded-Up Robust Features), which approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.
Proceedings ArticleDOI
Model globally, match locally: Efficient and robust 3D object recognition
TL;DR: A novel method is proposed that creates a global model description based on oriented point pair features and matches that model locally using a fast voting scheme, which allows using much sparser object and scene point clouds, resulting in very fast performance.
A Comparison of SIFT, PCA-SIFT and SURF
S. Govindarajulu,Kumar Reddy +1 more
TL;DR: KNN (K-Nearest Neighbor) and Random Sample Consensus (RANSAC) are added to the three robust feature detection methods in order to analyze the results of the methods‟ application in recognition.
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