scholarly journals An RGB-D-Based Cross-Field of View Pose Estimation System for a Free Flight Target in a Wind Tunnel

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Sheng Liu ◽  
Yuan Feng ◽  
Kang Shen ◽  
Yangqing Wang ◽  
Shengyong Chen
Keyword(s):  
Wind Tunnel ◽  
Real Time ◽  
Pose Estimation ◽  
Dynamic Testing ◽  
Estimation Method ◽  
Spatial Relationship ◽  
Field Of View ◽  
Free Flight ◽  
The Real ◽  
Estimation System

Estimating the real-time pose of a free flight aircraft in a complex wind tunnel environment is extremely difficult. Due to the high dynamic testing environment, complicated illumination condition, and the unpredictable motion of target, most general pose estimating methods will fail. In this paper, we introduce a cross-field of view (FOV) real-time pose estimation system, which provides high precision pose estimation of the free flight aircraft in the wind tunnel environment. Multiview live RGB-D streams are used in the system as input to ensure the measurement area can be fully covered. First, a multimodal initialization method is developed to measure the spatial relationship between the RGB-D camera and the aircraft. Based on all the input multimodal information, a so-called cross-FOV model is proposed to recognize the dominating sensor and accurately extract the foreground region in an automatic manner. Second, we develop an RGB-D-based pose estimation method for a single target, by which the 3D sparse points and the pose of the target can be simultaneously obtained in real time. Many experiments have been conducted, and an RGB-D image simulation based on 3D modeling is implemented to verify the effectiveness of our algorithm. Both the real scene’s and simulation scene’s experimental results demonstrate the effectiveness of our method.

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>

scholarly journals Global Appearance Applied to Visual Map Building and Path Estimation Using Multiscale Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-23 ◽  
Author(s):  
Francisco Amorós ◽  
Luis Payá ◽  
Oscar Reinoso ◽  
Walterio Mayol-Cuevas ◽  
Andrew Calway
Keyword(s):  
Pose Estimation ◽  
Visual Information ◽  
Multiscale Analysis ◽  
Spatial Arrangement ◽  
Field Of View ◽  
Indoor Environments ◽  
Map Building ◽  
Topological Map ◽  
Complete Field ◽  
Estimation System

In this work we present a topological map building and localization system for mobile robots based on global appearance of visual information. We include a comparison and analysis of global-appearance techniques applied to wide-angle scenes in retrieval tasks. Next, we define multiscale analysis, which permits improving the association between images and extracting topological distances. Then, a topological map-building algorithm is proposed. At first, the algorithm has information only of some isolated positions of the navigation area in the form of nodes. Each node is composed of a collection of images that covers the complete field of view from a certain position. The algorithm solves the node retrieval and estimates their spatial arrangement. With these aims, it uses the visual information captured along some routes that cover the navigation area. As a result, the algorithm builds a graph that reflects the distribution and adjacency relations between nodes (map). After the map building, we also propose a route path estimation system. This algorithm takes advantage of the multiscale analysis. The accuracy in the pose estimation is not reduced to the nodes locations but also to intermediate positions between them. The algorithms have been tested using two different databases captured in real indoor environments under dynamic conditions.

Enhanced real-time head pose estimation system for mobile device

2014 ◽  
Vol 21 (3) ◽  
pp. 281-293 ◽  
Author(s):  
Euclides N. Arcoverde Neto ◽  
Rafael M. Duarte ◽  
Rafael M. Barreto ◽  
João Paulo Magalhães ◽  
Carlos C.M. Bastos ◽  
...  
Keyword(s):  
Real Time ◽  
Pose Estimation ◽  
Mobile Device ◽  
Head Pose Estimation ◽  
Head Pose ◽  
Estimation System

scholarly journals A Fast Estimation Method for Satellite in Orbit Management

2020 ◽  
Vol 316 ◽  
pp. 04007
Author(s):  
Zhen Cui ◽  
Ye Ji ◽  
Bin Chen ◽  
Yang Yang
Keyword(s):  
Real Time ◽  
Satellite Telemetry ◽  
Estimation Method ◽  
Decisive Role ◽  
Telemetry Data ◽  
The Real ◽  
Fast Estimation ◽  
Fault Handling ◽  
Rapid Processing ◽  
The Impact

The management of satellites in orbit requires accurate and rapid processing to minimize the impact of failures. Especially for fault handling involving energy security, the real-time nature of the process plays a decisive role. This paper proposes a fast estimation method based on satellite telemetry data. Through the analysis of the shape of the telemetry curve, the geometric method, the correlation analysis of the telemetry data on the satellite, or the physical principles, the required parameters are quickly obtained to facilitate rapid emergency processing. This method is applied to satellite in-orbit management, which can greatly improve the real-time performance of fault processing, and has good engineering practical value.

scholarly journals InSAR Baseline Estimation for Gaofen-3 Real-Time DEM Generation

2018 ◽  
Author(s):  
Huan Lu ◽  
Zhiyong Suo ◽  
Zhenfang Li ◽  
Jinwei Xie ◽  
Qingjun Zhang
Keyword(s):  
Real Time ◽  
Estimation Method ◽  
Shuttle Radar Topography Mission ◽  
The Real ◽  
Digital Elevation ◽  
Orbit Fitting ◽  
Elevation Model ◽  
Interferometric Sar ◽  
Baseline Estimation ◽  
Baseline Error

For Interferometry Synthetic Aperture Radar (InSAR), the normal baseline is one of the main factors that affect the accuracy of the ground elevation. For Gaofen-3 (GF-3) InSAR processing, the poor accuracy of the real-time orbit determination resulting in a large baseline error, leads to the modulation error in azimuth and the slope error in range for timely Digital Elevation Model (DEM) generation. In order to address this problem, a baseline estimation method based on external DEM is proposed in this paper. Firstly, according to the characteristic of the real-time orbit of GF-3 images, orbit fitting is executed to remove the non-linear error factor. Secondly, the height errors are obtained in slant-range plane between Shuttle Radar Topography Mission (SRTM) DEM and the GF-3 generated DEM after orbit fitting. At the same time, the height errors are used to estimate the baseline error which has a linear variation. In this way, the orbit error can be calibrated by the estimated baseline error. Finally, DEM generation is performed by using the modified baseline and orbit. This procedure is implemented iteratively to achieve a higher accuracy DEM. Based on the results of GF-3 interferometric SAR data for Hebei, the effectiveness of the proposed algorithm is verified and the accuracy of GF-3 real-time DEM products can be improved extensively.

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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