Stereo vision for planetary rovers: Stochastic modeling to near real-time implementation

1992 ◽  
Vol 8 (1) ◽  
pp. 71-91 ◽  
Author(s):  
Larry Matthies
Keyword(s):  
Real Time ◽  
Stochastic Modeling ◽  
Stereo Vision ◽  
Planetary Rovers

Single- and Multi-FPGA Acceleration of Dense Stereo Vision for Planetary Rovers

2019 ◽  
Vol 18 (2) ◽  
pp. 1-27 ◽  
Author(s):  
George Lentaris ◽  
Konstantinos Maragos ◽  
Dimitrios Soudris ◽  
Xenophon Zabulis ◽  
Manolis Lourakis
Keyword(s):  
Stereo Vision ◽  
Planetary Rovers ◽  
Fpga Acceleration

<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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