Relative Pose Estimation under Monocular Vision in Rendezvous and Docking

2013 ◽  
Vol 433-435 ◽  
pp. 799-805 ◽  
Author(s):  
Hui Pan ◽  
Jian Yu Huang ◽  
Shi Yin Qin
Keyword(s):  
Estimation Algorithm ◽  
Monocular Vision ◽  
Estimation Accuracy ◽  
Estimation Efficiency ◽  
Autonomous Rendezvous ◽  
Space Programs ◽  
Efficiency Estimation ◽  
Relative Pose ◽  
Rendezvous And Docking ◽  
Model Approach

Autonomous rendezvous and docking (ARD) plays a very important role in planned space programs such as on-orbit construction and assembly, refueling of satellites, repairing or rescuing failed satellites, active removal of space debris, autonomous re-supply and crew exchange of space stations, and so on. However,the success of ARD rests with the estimation accuracy and efficiency of relative pose among various spacecraft in rendezvous and docking. In this paper, a high accuracy and efficiency estimation algorithm of relative pose of cooperative space targets is presented based on monocular vision imaging, in which a modified gravity model approach and multiple targets tracking methods are employed to improve the accuracy of feature extraction and enhance the estimation efficiency, moreover the Levenberg-Marquardt method (LMM) is used to achieve a well global convergence. The comprehensive experiment results demonstrate its outstanding predominance in estimation accuracy and efficiency.

scholarly journals Novel MARG-Sensor Orientation Estimation Algorithm Using Fast Kalman Filter

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Siwen Guo ◽  
Jin Wu ◽  
Zuocai Wang ◽  
Jide Qian
Keyword(s):  
Kalman Filter ◽  
Process Model ◽  
Attitude Determination ◽  
Angular Rate ◽  
Estimation Algorithm ◽  
Measurement Model ◽  
Estimation Accuracy ◽  
Orientation Estimation ◽  
Kinematic Equation ◽  
Sensor Orientation

Orientation estimation from magnetic, angular rate, and gravity (MARG) sensor array is a key problem in mechatronic-related applications. This paper proposes a new method in which a quaternion-based Kalman filter scheme is designed. The quaternion kinematic equation is employed as the process model. With our previous contributions, we establish the measurement model of attitude quaternion from accelerometer and magnetometer, which is later proved to be the fastest (computationally) one among representative attitude determination algorithms of such sensor combination. Variance analysis is later given enabling the optimal updating of the proposed filter. The algorithm is implemented on real-world hardware where experiments are carried out to reveal the advantages of the proposed method with respect to conventional ones. The proposed approach is also validated on an unmanned aerial vehicle during a real flight. Results show that the proposed one is faster than any other Kalman-based ones and even faster than some complementary ones while the attitude estimation accuracy is maintained.

WTLS-Bayes Filter-based Relative Pose Estimation for Space Non-cooperative Unknown Target Without a Priori Knowledge

2021 ◽  
Author(s):  
CHENGGUANG ZHU ◽  
zhongpai Gao ◽  
Jiankang Zhao ◽  
Haihui Long ◽  
Chuanqi Liu
Keyword(s):  
Pose Estimation ◽  
A Priori ◽  
Convergence Time ◽  
Estimation Accuracy ◽  
A Priori Knowledge ◽  
One Step ◽  
Relative Pose ◽  
The One ◽  
Negative Factors ◽  
Priori Knowledge

Abstract The relative pose estimation of a space noncooperative target is an attractive yet challenging task due to the complexity of the target background and illumination, and the lack of a priori knowledge. Unfortunately, these negative factors have a grave impact on the estimation accuracy and the robustness of filter algorithms. In response, this paper proposes a novel filter algorithm to estimate the relative pose to improve the robustness based on a stereovision system. First, to obtain a coarse relative pose, the weighted total least squares (WTLS) algorithm is adopted to estimate the relative pose based on several feature points. The resulting relative pose is fed into the subsequent filter scheme as observation quantities. Second, the classic Bayes filter is exploited to estimate the relative state except for moment-of-inertia ratios. Additionally, the one-step prediction results are used as feedback for WTLS initialization. The proposed algorithm successfully eliminates the dependency on continuous tracking of several fixed points. Finally, comparison experiments demonstrate that the proposed algorithm presents a better performance in terms of robustness and convergence time.

scholarly journals Estimating Resting Core Temperature Using Heart Rate

2018 ◽  
Vol 1 (2) ◽  
pp. 79-86 ◽  
Author(s):  
David P. Looney ◽  
Mark J. Buller ◽  
Andrei V. Gribok ◽  
Jayme L. Leger ◽  
Adam W. Potter ◽  
...  
Keyword(s):  
Heart Rate ◽  
Core Temperature ◽  
Estimation Algorithm ◽  
Quadratic Equation ◽  
Estimation Accuracy ◽  
Future Research ◽  
Accuracy And Precision ◽  
Military Populations ◽  
Thermal Work ◽  
Ct Data

ECTemp™ is a heart rate (HR)-based core temperature (CT) estimation algorithm mainly used as a real-time thermal-work strain indicator in military populations. ECTemp™ may also be valuable for resting CT estimation, which is critical for circadian rhythm research. This investigation developed and incorporated a sigmoid equation into ECTemp™ to better estimate resting CT. HR and CT data were collected over two calorimeter test trials from 16 volunteers (age, 23 ± 3 yrs; height, 1.72 ± 0.07 m; body mass, 68.5 ± 8.1 kg) during periods of sleep and inactivity. Half of the test trials were combined with ECTemp™’s original development dataset to train the new sigmoid model while the other was used for model validation. Models were compared by their estimation accuracy and precision. While both models produced accurate CT estimates, the sigmoid model had a smaller bias (−0.04 ± 0.26°C vs. −0.19 ± 0.29°C) and root mean square error (RMSE; 0.26°C vs. 0.35°C). ECTemp™ is a validated HR-based resting CT estimation algorithm. The new sigmoid equation corrects lower CT estimates while producing nearly identical estimates to the original quadratic equation at higher CT. The demonstrated accuracy of ECTemp™ encourages future research to explore the algorithm’s potential as a non-invasive means of tracking CT circadian rhythms.

Robust high-degree cubature information filter and its application to trajectory estimation for ballistic missile

2017 ◽  
Vol 232 (12) ◽  
pp. 2364-2377 ◽  
Author(s):  
Xiaogang Wang ◽  
Wutao Qin ◽  
Naigang Cui ◽  
Yu Wang
Keyword(s):  
State Estimation ◽  
Estimation Algorithm ◽  
Ballistic Missile ◽  
Measurement Noise ◽  
Estimation Accuracy ◽  
Trajectory Estimation ◽  
Information Filter ◽  
Non Gaussian ◽  
Time Update ◽  
High Degree

This paper presents a new recursive filter algorithm, the robust high-degree cubature information filter, which can provide reliable state estimation in the presence of non-Gaussian measurement noise. The novel algorithm is developed in the framework of the conventional information filter. The fifth-degree Cubature rule is utilized to improve the estimation accuracy and numerical stability during the time update, while the Huber technique is adopted in the measurements update stage. As the Huber technique is a combined minimum l1 and l2 norm estimation algorithm, the proposed algorithm could exhibit robustness to the non-Gaussian measurement noise, especially the glint noise. In addition, Monte Carlo simulation and the trajectory estimation for ballistic missile experiments demonstrate that the robust high-degree cubature information filter can provide improved state estimation performance over extended information filter and high-degree cubature information filter.

scholarly journals A Hardware-Friendly Optical Flow-Based Time-to-Collision Estimation Algorithm

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 807
Author(s):  
Cong Shi ◽  
Zhuoran Dong ◽  
Shrinivas Pundlik ◽  
Gang Luo
Keyword(s):  
Embedded System ◽  
Optical Flow ◽  
Random Forests ◽  
Visual Motion ◽  
Hardware Acceleration ◽  
Ground Truth ◽  
Estimation Algorithm ◽  
Estimation Accuracy ◽  
Time To Collision ◽  
Dense Optical Flow

This work proposes a hardware-friendly, dense optical flow-based Time-to-Collision (TTC) estimation algorithm intended to be deployed on smart video sensors for collision avoidance. The algorithm optimized for hardware first extracts biological visual motion features (motion energies), and then utilizes a Random Forests regressor to predict robust and dense optical flow. Finally, TTC is reliably estimated from the divergence of the optical flow field. This algorithm involves only feed-forward data flows with simple pixel-level operations, and hence has inherent parallelism for hardware acceleration. The algorithm offers good scalability, allowing for flexible tradeoffs among estimation accuracy, processing speed and hardware resource. Experimental evaluation shows that the accuracy of the optical flow estimation is improved due to the use of Random Forests compared to existing voting-based approaches. Furthermore, results show that estimated TTC values by the algorithm closely follow the ground truth. The specifics of the hardware design to implement the algorithm on a real-time embedded system are laid out.

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