scholarly journals GPU-based real-time trinocular stereo vision

2013 ◽  
Author(s):  
Yuanbin Yao ◽  
R. J. Linton ◽  
Taskin Padir
Keyword(s):  
Real Time ◽  
Stereo Vision ◽  
Trinocular Stereo

scholarly journals High Precision Optical Tracking System Based on near Infrared Trinocular Stereo Vision

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2528
Author(s):  
Songlin Bi ◽  
Yonggang Gu ◽  
Jiaqi Zou ◽  
Lianpo Wang ◽  
Chao Zhai ◽  
...  
Keyword(s):  
High Precision ◽  
Stereo Vision ◽  
Near Infrared ◽  
Nearest Neighbor ◽  
Tracking System ◽  
Optical Tracking ◽  
Tracking Accuracy ◽  
Optical Tracking System ◽  
Dynamic Tracking ◽  
Trinocular Stereo

A high precision optical tracking system (OTS) based on near infrared (NIR) trinocular stereo vision (TSV) is presented in this paper. Compared with the traditional OTS on the basis of binocular stereo vision (BSV), hardware and software are improved. In the hardware aspect, a NIR TSV platform is built, and a new active tool is designed. Imaging markers of the tool are uniform and complete with large measurement angle (>60°). In the software aspect, the deployment of extra camera brings high computational complexity. To reduce the computational burden, a fast nearest neighbor feature point extraction algorithm (FNNF) is proposed. The proposed method increases the speed of feature points extraction by hundreds of times over the traditional pixel-by-pixel searching method. The modified NIR multi-camera calibration method and 3D reconstruction algorithm further improve the tracking accuracy. Experimental results show that the calibration accuracy of the NIR camera can reach 0.02%, positioning accuracy of markers can reach 0.0240 mm, and dynamic tracking accuracy can reach 0.0938 mm. OTS can be adopted in high-precision dynamic tracking.

<i>Real-time Corn Grain Measurement Device Using Stereo Vision</i>

2019 ◽  
Author(s):  
Matthew Benjamin Rogers ◽  
Robert Clark Stevens
Keyword(s):  
Real Time ◽  
Stereo Vision ◽  
Measurement Device ◽  
Corn Grain

scholarly journals Learning-Type Anchors-Driven Pose Estimation for the Autolanding Fixed-Wing UAVs

2021 ◽  
Author(s):  
Dengqing Tang ◽  
Lincheng Shen ◽  
Xiaojiao Xiang ◽  
Han Zhou ◽  
Tianjiang Hu
Keyword(s):  
Real Time ◽  
Pose Estimation ◽  
Stereo Vision ◽  
Vision System ◽  
Estimation Method ◽  
Satellite System ◽  
Training Dataset ◽  
Estimation Accuracy ◽  
The Impact ◽  
Outdoor Experiments

<p>We propose a learning-type anchors-driven real-time pose estimation method for the autolanding fixed-wing unmanned aerial vehicle (UAV). The proposed method enables online tracking of both position and attitude by the ground stereo vision system in the Global Navigation Satellite System denied environments. A pipeline of convolutional neural network (CNN)-based UAV anchors detection and anchors-driven UAV pose estimation are employed. To realize robust and accurate anchors detection, we design and implement a Block-CNN architecture to reduce the impact of the outliers. With the basis of the anchors, monocular and stereo vision-based filters are established to update the UAV position and attitude. To expand the training dataset without extra outdoor experiments, we develop a parallel system containing the outdoor and simulated systems with the same configuration. Simulated and outdoor experiments are performed to demonstrate the remarkable pose estimation accuracy improvement compared with the conventional Perspective-N-Points solution. In addition, the experiments also validate the feasibility of the proposed architecture and algorithm in terms of the accuracy and real-time capability requirements for fixed-wing autolanding UAVs.</p>

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