Robust and accurate online pose estimation algorithm via efficient three‐dimensional collinearity model

2013 ◽  
Vol 7 (5) ◽  
pp. 382-393 ◽  
Author(s):  
Baojie Fan ◽  
Yingkui Du ◽  
Yang Cong
Keyword(s):  
Pose Estimation ◽  
Three Dimensional ◽  
Estimation Algorithm

Head Pose Estimation Algorithm Based on Structured Light Three-Dimensional Reconstruction

2020 ◽  
Vol 57 (18) ◽  
pp. 181008
Author(s):  
钟俊宇 Zhong Junyu ◽  
邱健 Qiu Jian ◽  
韩鹏 Han Peng ◽  
骆开庆 Luo Kaiqing ◽  
彭力 Peng Li ◽  
...  
Keyword(s):  
Pose Estimation ◽  
Structured Light ◽  
Three Dimensional ◽  
Estimation Algorithm ◽  
Head Pose Estimation ◽  
Three Dimensional Reconstruction ◽  
Head Pose ◽  
Dimensional Reconstruction

Heterogeneous Multisensor Fusion for Mobile Platform Three-Dimensional Pose Estimation

2017 ◽  
Vol 139 (7) ◽  
Author(s):  
Hanieh Deilamsalehy ◽  
Timothy C. Havens ◽  
Joshua Manela
Keyword(s):  
Pose Estimation ◽  
Three Dimensional ◽  
Estimation Algorithm ◽  
Estimation Accuracy ◽  
Two Dimensional ◽  
Localization Algorithm ◽  
3D Pose Estimation ◽  
Accurate Localization ◽  
Localization And Mapping ◽  
Aerial Vehicle

Precise, robust, and consistent localization is an important subject in many areas of science such as vision-based control, path planning, and simultaneous localization and mapping (SLAM). To estimate the pose of a platform, sensors such as inertial measurement units (IMUs), global positioning system (GPS), and cameras are commonly employed. Each of these sensors has their strengths and weaknesses. Sensor fusion is a known approach that combines the data measured by different sensors to achieve a more accurate or complete pose estimation and to cope with sensor outages. In this paper, a three-dimensional (3D) pose estimation algorithm is presented for a unmanned aerial vehicle (UAV) in an unknown GPS-denied environment. A UAV can be fully localized by three position coordinates and three orientation angles. The proposed algorithm fuses the data from an IMU, a camera, and a two-dimensional (2D) light detection and ranging (LiDAR) using extended Kalman filter (EKF) to achieve accurate localization. Among the employed sensors, LiDAR has not received proper attention in the past; mostly because a two-dimensional (2D) LiDAR can only provide pose estimation in its scanning plane, and thus, it cannot obtain a full pose estimation in a 3D environment. A novel method is introduced in this paper that employs a 2D LiDAR to improve the full 3D pose estimation accuracy acquired from an IMU and a camera, and it is shown that this method can significantly improve the precision of the localization algorithm. The proposed approach is evaluated and justified by simulation and real world experiments.

Pose Estimation of Cooperative Targets for Space Exploration Task Based on Binocular Visual Measurement

2013 ◽  
Vol 333-335 ◽  
pp. 268-274
Author(s):  
Jing Jing Wang ◽  
Jian Yu Huang ◽  
Shi Yin Qin
Keyword(s):  
Pose Estimation ◽  
Three Dimensional ◽  
Least Square Method ◽  
Estimation Algorithm ◽  
High Accuracy ◽  
Least Square ◽  
Estimation Accuracy ◽  
Feature Points ◽  
Well Performance ◽  
Visual Measurement

In this paper, a high accuracy and efficiency pose estimation algorithm is proposed for space cooperative targets in RVD based on binocular visual measurement. At first, the scheme of visual measurement toward RVD is presented and the environment conditions and performance requirement are analysed and discussed. Then the relationship of pose estimation with detection and tracking is studied to give an implementing strategy of pose estimation with high accuracy and efficiency. Moreover, the key point is focused on the pose estimation of cooperative targets, in which a stereo vision mapping relation between three dimensionl coordinates of spacial feature points of cooperative targets and their corresponding image coordinates is established, then the least square method is employed to estimate the three-dimensional coordinates of feature points so as to calculate the relative position and attitude between tracking spacecraft and target spacecraft with high precision, finally a series of experimental resluts indicate that the proposed pose estimation algorithm under binocular visual measurement demonstrates well performance in the estimation accuracy, anti-noise and real-time thus can achieve the application requriements of RVD under binocular visual measurement.

scholarly journals TRAJECTORY SIMULATION OF BADMINTON ROBOT BASED ON FRACTAL BROWN MOTION

Fractals ◽  
2020 ◽  
Vol 28 (08) ◽  
pp. 2040021
Author(s):  
GAOYUAN CUI ◽  
BIN ZHANG ◽  
RODRIGUES MARLENE
Keyword(s):  
Real Time ◽  
Pose Estimation ◽  
Three Dimensional ◽  
Estimation Algorithm ◽  
Research Direction ◽  
Future Research ◽  
Detection Accuracy ◽  
Binocular Stereo Vision ◽  
Executive Mechanism ◽  
Brown Motion

This paper focuses on the design of badminton robots, and designs high-precision binocular stereo vision synchronous acquisition system hardware and multithreaded acquisition programs to ensure the left and right camera exposure synchronization and timely reading of data. Aiming at specific weak moving targets, a shape-based Brown motion model based on dynamic threshold adjustment based on singular value decomposition is proposed, and a discriminative threshold is set according to the similarity between the background and the foreground to improve detection accuracy. The three-dimensional trajectory points are extended by Kalman filter and the kinematics equation of badminton is established. The parameters of the kinematics equation of badminton are solved by the method of least squares. Based on the fractal Brownian motion algorithm, a real-time robot pose estimation algorithm is proposed to realize the real-time accurate pose estimation of the robot. A PID control model for the badminton robot executive mechanism is established between the omnidirectional wheel speed and the robot’s translation and rotation movements to achieve the precise movement of the badminton robot. All the algorithms can meet the system’s requirements for real-time performance, realize the badminton robot’s simple hit to the ball, and prospect the future research direction.

scholarly journals Deep Learning-Based Pose Estimation of Apples for Inspection in Logistic Centers Using Single-Perspective Imaging

Processes ◽  
2019 ◽  
Vol 7 (7) ◽  
pp. 424 ◽  
Author(s):  
Lino Antoni Giefer ◽  
Juan Daniel Arango Castellanos ◽  
Mohammad Mohammadzadeh Babr ◽  
Michael Freitag
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Pose Estimation ◽  
Three Dimensional ◽  
Estimation Algorithm ◽  
Single Shot ◽  
Network Architectures ◽  
Image Area ◽  
Natural Variations ◽  
Bounding Boxes

Fruit packaging is a time-consuming task due to its low automation level. The gentle handling required by some kinds of fruits and their natural variations complicates the implementation of automated quality controls and tray positioning for final packaging. In this article, we propose a method for the automatic localization and pose estimation of apples captured by a Red-Green-Blue (RGB) camera using convolutional neural networks. Our pose estimation algorithm uses a cascaded structure composed of two independent convolutional neural networks: one for the localization of apples within the images and a second for the estimation of the three-dimensional rotation of the localized and cropped image area containing an apple. We used a single shot multi-box detector to find the bounding boxes of the apples in the images. Lie algebra is used for the regression of the rotation, which represents an innovation in this kind of application. We compare the performances of four different network architectures and show that this kind of representation is more suitable than using state-of-the-art quaternions. By using this method, we achieved a promising accuracy for the rotation regression of 98.36%, considering an error range lower than 15 degrees, forming a base for the automation of fruit packing systems.

Addressing pose estimation issues for machine vision based UAV autonomous serial refuelling

2007 ◽  
Vol 111 (1120) ◽  
pp. 389-396 ◽  
Author(s):  
G. Campa ◽  
M. R. Napolitano ◽  
M. Perhinschi ◽  
M. L. Fravolini ◽  
L. Pollini ◽  
...  
Keyword(s):  
Machine Vision ◽  
Pose Estimation ◽  
Estimation Algorithm ◽  
Computational Effort ◽  
Unmanned Aircraft ◽  
Light Sources ◽  
Estimation Algorithms ◽  
Trade Offs ◽  
Labeling Algorithms ◽  
Selection Of

Abstract This paper describes the results of an effort on the analysis of the performance of specific ‘pose estimation’ algorithms within a Machine Vision-based approach for the problem of aerial refuelling for unmanned aerial vehicles. The approach assumes the availability of a camera on the unmanned aircraft for acquiring images of the refuelling tanker; also, it assumes that a number of active or passive light sources – the ‘markers’ – are installed at specific known locations on the tanker. A sequence of machine vision algorithms on the on-board computer of the unmanned aircraft is tasked with the processing of the images of the tanker. Specifically, detection and labeling algorithms are used to detect and identify the markers and a ‘pose estimation’ algorithm is used to estimate the relative position and orientation between the two aircraft. Detailed closed-loop simulation studies have been performed to compare the performance of two ‘pose estimation’ algorithms within a simulation environment that was specifically developed for the study of aerial refuelling problems. Special emphasis is placed on the analysis of the required computational effort as well as on the accuracy and the error propagation characteristics of the two methods. The general trade offs involved in the selection of the pose estimation algorithm are discussed. Finally, simulation results are presented and analysed.

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