scholarly journals A Novel Method for the Absolute Pose Problem with Pairwise Constraints

2019 ◽  
Vol 11 (24) ◽  
pp. 3007 ◽  
Author(s):  
Yinlong Liu ◽  
Xuechen Li ◽  
Manning Wang ◽  
Alois Knoll ◽  
Guang Chen ◽  
...  
Keyword(s):  
Pose Estimation ◽  
Optimal Algorithm ◽  
Optimization Method ◽  
Estimation Problem ◽  
Parameter Estimation Problem ◽  
Real World Data ◽  
Pairwise Constraints ◽  
Optimal Rotation ◽  
Novel Method ◽  
The Absolute

Absolute pose estimation from corrupted point correspondences is typically a problem of estimating parameters from outlier-contaminated data. Conventionally, for a fixed dimensionality d and the number of measurements N, a robust estimation problem cannot be solved exactly faster than O ( N d ) . Furthermore, it is almost impossible to remove d from the exponent of the runtime of a globally optimal algorithm. However, absolute pose estimation is a geometric parameter estimation problem, and thus has special constraints. In this paper, we consider pairwise constraints and propose a novel algorithm utilizing global optimization method Branch-and-Bound (BnB) for solving the absolute pose estimation problem. Concretely, we first decouple the rotation and the translation subproblems by utilizing the pairwise constraints, and then we solve the rotation subproblem using the BnB algorithm. Lastly, we estimate the translation based on the optimal rotation by using another BnB algorithm. The proposed algorithm has an approximately linear complexity in the number of correspondences at a given outlier ratio. The advantages of our method were demonstrated via thorough testing on both synthetic and real-world data.

scholarly journals Decentralized Optimization for a Novel Control Structure of HVAC System

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Shiqiang Wang ◽  
Jianchun Xing ◽  
Ziyan Jiang ◽  
Juelong Li
Keyword(s):  
Control System ◽  
Decentralized Control ◽  
Control Structure ◽  
Optimization Method ◽  
Labor Cost ◽  
Smart Device ◽  
Hvac System ◽  
Decentralized Optimization ◽  
System A ◽  
Novel Method

A decentralized control structure is introduced into the heating, ventilation, and air conditioning (HVAC) system to solve the high maintenance and labor cost problem in actual engineering. Based on this new control system, a decentralized optimization method is presented for sensor fault repair and optimal group control of HVAC equipment. Convergence property of the novel method is theoretically analyzed considering both convex and nonconvex systems with constraints. In this decentralized control system, traditional device is fitted with a control chip such that it becomes a smart device. The smart device can communicate and operate collaboratively with the other devices to accomplish some designated tasks. The effectiveness of the presented method is verified by simulations and hardware tests.

A RELAX Based Method for Spotlight SAR Imaging

2012 ◽  
Vol 500 ◽  
pp. 362-367
Author(s):  
Xiao Zhen Ren ◽  
Yao Qin ◽  
Hong Liang Fu
Keyword(s):  
Parameter Estimation ◽  
Estimation Problem ◽  
Noisy Environment ◽  
Parameter Estimation Problem ◽  
Imaging Algorithm ◽  
Imaging Results ◽  
Sar Imaging ◽  
Simulation Results ◽  
Noisy Condition

The imaging problem of spotlight SAR is converted to a parameter estimation problem of several monochromatic signals in additive white Gaussian noisy condition in this paper. Moreover, a spotlight SAR imaging algorithm based on RELAX is presented in detail. Traditional polar format algorithm and the presented method are applied to spotlight SAR imaging respectively, and the imaging results are compared. Simulation results show that the polar format algorithm doesn’t give satisfactory imaging results, while the proposed method adapts the noisy environment better and obtains better results.

A Novel Method for Preoperative 6D Pose Estimation of Rat Skull Based on 3D Vision Techniques

2021 ◽  
Author(s):  
Yujun Wu ◽  
Hanwei Chen ◽  
Bo Han ◽  
Chao Liu ◽  
Xinjun Sheng
Keyword(s):  
Pose Estimation ◽  
3D Vision ◽  
Novel Method

scholarly journals IMAGE-BASED ORIENTATION DETERMINATION OF MOBILE SENSOR PLATFORMS

2021 ◽  
Vol XLIII-B1-2021 ◽  
pp. 215-220
Author(s):  
O. Hasler ◽  
S. Nebiker
Keyword(s):  
Sensor Fusion ◽  
Pose Estimation ◽  
Reference System ◽  
Laser Scanner ◽  
Relative Orientation ◽  
Robotic Platform ◽  
Sensor Platforms ◽  
Local Reference ◽  
The Absolute ◽  
Additional Approach

Abstract. Estimating the pose of a mobile robotic platform is a challenging task, especially when the pose needs to be estimated in a global or local reference frame and when the estimation has to be performed while the platform is moving. While the position of a platform can be measured directly via modern tachymetry or with the help of a global positioning service GNSS, the absolute platform orientation is harder to derive. Most often, only the relative orientation is estimated with the help of a sensor mounted on the robotic platform such as an IMU, with one or multiple cameras, with a laser scanner or with a combination of any of those. Then, a sensor fusion of the relative orientation and the absolute position is performed. In this work, an additional approach is presented: first, an image-based relative pose estimation with frames from a panoramic camera using a state-of-the-art visual odometry implementation is performed. Secondly, the position of the platform in a reference system is estimated using motorized tachymetry. Lastly, the absolute orientation is calculated using a visual marker, which is placed in the space, where the robotic platform is moving. The marker can be detected in the camera frame and since the position of this marker is known in the reference system, the absolute pose can be estimated. To improve the absolute pose estimation, a sensor fusion is conducted. Results with a Lego model train as a mobile platform show, that the trajectory of the absolute pose calculated independently with four different markers have a deviation < 0.66 degrees 50% of the time and that the average difference is < 1.17 degrees. The implementation is based on the popular Robotic Operating System ROS.

scholarly journals A Closed-Form Solution for Estimating the Accuracy of Circular Feature’ Pose for Object 2D-3D Pose Estimation System

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Cui Li ◽  
Derong Chen ◽  
Jiulu Gong ◽  
Yangyu Wu
Keyword(s):  
Closed Form ◽  
Covariance Matrix ◽  
Pose Estimation ◽  
Closed Form Solution ◽  
Optimization Method ◽  
Form Solution ◽  
Synthetic Image ◽  
Point Selection ◽  
3D Pose Estimation ◽  
Estimation System

Many objects in the real world have circular feature. In general, circular feature’s pose is represented by 5-DoF (degree of freedom) vector ξ = X , Y , Z , α , β T . It is a difficult task to measure the accuracy of circular feature’s pose in each direction and the correlation between each direction. This paper proposes a closed-form solution for estimating the accuracy of pose transformation of circular feature. The covariance matrix of ξ is used to measure the accuracy of the pose. The relationship between the pose of the circular feature of 3D object and the 2D points is analyzed to yield an implicit function, and then Gauss–Newton theorem is employed to compute the partial derivatives of the function with respect to such point, and after that the covariance matrix is computed from both the 2D points and the extraction error. In addition, the method utilizes the covariance matrix of 5-DoF circular feature’s pose variables to optimize the pose estimator. Based on pose covariance, minimize the mean square error (Min-MSE) metric is introduced to guide good 2D imaging point selection, and the total amount of noise introduced into the pose estimator can be reduced. This work provides an accuracy method for object 2D-3D pose estimation using circular feature. At last, the effectiveness of the method for estimating the accuracy is validated based on both random data sets and synthetic images. Various synthetic image sequences are illustrated to show the performance and advantages of the proposed pose optimization method for estimating circular feature’s pose.

Global-Search Distributed-Gauss-Newton Optimization Method and Its Integration With the Randomized-Maximum-Likelihood Method for Uncertainty Quantification of Reservoir Performance

SPE Journal ◽  
2018 ◽  
Vol 23 (05) ◽  
pp. 1496-1517 ◽  
Author(s):  
Chaohui Chen ◽  
Guohua Gao ◽  
Ruijian Li ◽  
Richard Cao ◽  
Tianhong Chen ◽  
...  
Keyword(s):  
History Matching ◽  
Optimization Method ◽  
Global Search ◽  
Real Field ◽  
Production Data ◽  
Finite Difference Approximations ◽  
Production Forecasting ◽  
Novel Method ◽  
Randomized Maximum Likelihood ◽  
Search Capability

Summary Although it is possible to apply traditional optimization algorithms together with the randomized-maximum-likelihood (RML) method to generate multiple conditional realizations, the computation cost is high. This paper presents a novel method to enhance the global-search capability of the distributed-Gauss-Newton (DGN) optimization method and integrates it with the RML method to generate multiple realizations conditioned to production data synchronously. RML generates samples from an approximate posterior by minimizing a large ensemble of perturbed objective functions in which the observed data and prior mean values of uncertain model parameters have been perturbed with Gaussian noise. Rather than performing these minimizations in isolation using large sets of simulations to evaluate the finite-difference approximations of the gradients used to optimize each perturbed realization, we use a concurrent implementation in which simulation results are shared among different minimization tasks whenever these results are helping to converge to the global minimum of a specific minimization task. To improve sharing of results, we relax the accuracy of the finite-difference approximations for the gradients with more widely spaced simulation results. To avoid trapping in local optima, a novel method to enhance the global-search capability of the DGN algorithm is developed and integrated seamlessly with the RML formulation. In this way, we can improve the quality of RML conditional realizations that sample the approximate posterior. The proposed work flow is first validated with a toy problem and then applied to a real-field unconventional asset. Numerical results indicate that the new method is very efficient compared with traditional methods. Hundreds of data-conditioned realizations can be generated in parallel within 20 to 40 iterations. The computational cost (central-processing-unit usage) is reduced significantly compared with the traditional RML approach. The real-field case studies involve a history-matching study to generate history-matched realizations with the proposed method and an uncertainty quantification of production forecasting using those conditioned models. All conditioned models generate production forecasts that are consistent with real-production data in both the history-matching period and the blind-test period. Therefore, the new approach can enhance the confidence level of the estimated-ultimate-recovery (EUR) assessment using production-forecasting results generated from all conditional realizations, resulting in significant business impact.

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