A Low-Cost Stereo Vision System for Real-Time Pose Estimation and its Application for Robot Tracking

2014 ◽  
Vol 619 ◽  
pp. 249-253
Author(s):  
Viboon Sangveraphunsiri ◽  
Pongsakon Bamrungthai
Keyword(s):  
Real Time ◽  
Pose Estimation ◽  
Stereo Vision ◽  
Low Cost ◽  
Coordinate Frame ◽  
Search Problem ◽  
Base System ◽  
Reconstruction Process ◽  
Robot Tracking ◽  
Planar Target

In this paper, a 3-D pose estimation system by using stereo vision with low-cost devices is presented. It is developed as a base system for application development. Two webcams and a planar target with circular markers are used to reduce development cost and computational complexity. To avoid correspondence search problem, user has to select regions of interest (ROI’s) of each marker on the two images in the same sequence before starting the 3-D reconstruction process. Linear triangulation method is applied for 3-D position calculation of each marker. These positions and the positions of the markers referenced in the planar target coordinate frame are used for pose estimation by using least-squares fitting algorithm to obtain the position and orientation of the planar target. The system can be applied for robot tracking as shown in the experiments. The experimental results validate the system’s ability to estimate object pose in real-time with minimum system frequency of 25 Hz.

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>

Low-cost FPGA stereo vision system for real time disparity maps calculation

2012 ◽  
Vol 36 (4) ◽  
pp. 281-288 ◽  
Author(s):  
Paolo Zicari ◽  
Stefania Perri ◽  
Pasquale Corsonello ◽  
Giuseppe Cocorullo
Keyword(s):  
Real Time ◽  
Stereo Vision ◽  
Vision System ◽  
Low Cost ◽  
Stereo Vision System ◽  
Disparity Maps

Real-time mobile robot navigation based on stereo vision and low-cost GPS

2017 ◽  
Vol 2017 (9) ◽  
pp. 10-15 ◽  
Author(s):  
Soonhac Hong ◽  
Ming Li ◽  
Miao Liao ◽  
Peter van Beek
Keyword(s):  
Mobile Robot ◽  
Real Time ◽  
Stereo Vision ◽  
Robot Navigation ◽  
Low Cost ◽  
Mobile Robot Navigation

Wide-angle and long-range real time pose estimation: A comparison between monocular and stereo vision systems

2017 ◽  
Vol 48 ◽  
pp. 159-168 ◽  
Author(s):  
Pasquale Ferrara ◽  
Alessandro Piva ◽  
Fabrizio Argenti ◽  
Junya Kusuno ◽  
Marta Niccolini ◽  
...  
Keyword(s):  
Real Time ◽  
Long Range ◽  
Pose Estimation ◽  
Stereo Vision ◽  
Wide Angle ◽  
Vision Systems

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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