scholarly journals Optimal Stochastic Sensor Error Modeling based on Actual Impact on Quality of GNSS-INS Integrated Navigation

2019 ◽  
Author(s):  
Mehran Khaghani ◽  
Stéphane Guerrier ◽  
Jan Skaloud ◽  
Yuming Zhang
Keyword(s):  
Error Modeling ◽  
Integrated Navigation ◽  
Sensor Error ◽  
Actual Impact

scholarly journals Robust Visual-Inertial Integrated Navigation System Aided by Online Sensor Model Adaption for Autonomous Ground Vehicles in Urban Areas

2020 ◽  
Vol 12 (10) ◽  
pp. 1686 ◽  
Author(s):  
Xiwei Bai ◽  
Weisong Wen ◽  
Li-Ta Hsu
Keyword(s):  
Navigation System ◽  
Urban Areas ◽  
Feature Tracking ◽  
Measurement Model ◽  
Integrated Navigation ◽  
Dynamic Features ◽  
Integrated Navigation System ◽  
M Estimator ◽  
Dynamic Objects

The visual-inertial integrated navigation system (VINS) has been extensively studied over the past decades to provide accurate and low-cost positioning solutions for autonomous systems. Satisfactory performance can be obtained in an ideal scenario with sufficient and static environment features. However, there are usually numerous dynamic objects in deep urban areas, and these moving objects can severely distort the feature-tracking process which is critical to the feature-based VINS. One well-known method that mitigates the effects of dynamic objects is to detect vehicles using deep neural networks and remove the features belonging to surrounding vehicles. However, excessive feature exclusion can severely distort the geometry of feature distribution, leading to limited visual measurements. Instead of directly eliminating the features from dynamic objects, this study proposes to adopt the visual measurement model based on the quality of feature tracking to improve the performance of the VINS. First, a self-tuning covariance estimation approach is proposed to model the uncertainty of each feature measurement by integrating two parts: (1) the geometry of feature distribution (GFD); (2) the quality of feature tracking. Second, an adaptive M-estimator is proposed to correct the measurement residual model to further mitigate the effects of outlier measurements, like the dynamic features. Different from the conventional M-estimator, the proposed method effectively alleviates the reliance on the excessive parameterization of the M-estimator. Experiments were conducted in typical urban areas of Hong Kong with numerous dynamic objects. The results show that the proposed method could effectively mitigate the effects of dynamic objects and improved accuracy of the VINS is obtained when compared with the conventional VINS method.

Wavelet-Based Improvements for Inertial Sensor Error Modeling

2016 ◽  
Vol 65 (12) ◽  
pp. 2693-2700 ◽  
Author(s):  
Stephane Guerrier ◽  
Roberto Molinari ◽  
Yannick Stebler
Keyword(s):  
Inertial Sensor ◽  
Error Modeling ◽  
Sensor Error

scholarly journals Robust Visual-Inertial Integrated Navigation System Aided by Online Sensor Model Adaption for Autonomous Ground Vehicles in Urban Areas

2020 ◽  
Author(s):  
Xiwei Bai ◽  
Weisong Wen ◽  
Li-Ta Hsu
Keyword(s):  
Navigation System ◽  
Urban Areas ◽  
Feature Tracking ◽  
Measurement Model ◽  
Integrated Navigation ◽  
Dynamic Features ◽  
Integrated Navigation System ◽  
M Estimator ◽  
Dynamic Objects

Visual-inertial integrated navigation system (VINS) has been extensively studied over the past decades to provide accurate and low-cost positioning solutions for autonomous systems. Satisfactory performance can be obtained in an ideal scenario with sufficient and static environment features. However, there are usually numerous dynamic objects in deep urban areas, and these moving objects can severely distort the feature tracking process which is fatal to the feature-based VINS. The well-known method mitigates the effects of dynamic objects is to detect the vehicles using deep neural networks and remove the features belongs to the surrounding vehicle. However, excessive exclusion of features can severely distort the geometry of feature distribution, leading to limited visual measurements. Instead of directly eliminating the features from dynamic objects, this paper proposes to adopt the visual measurement model based on the quality of feature tracking to improve the performance of VINS. Firstly, a self-tuning covariance estimation approach is proposed to model the uncertainty of each feature measurements by integrating two parts: 1) the geometry of feature distribution (GFD), 2) the quality of feature tracking. Secondly, an adaptive M-estimator is proposed to correct the measurement residual model to further mitigate the impacts of outlier measurements, such as the dynamic features. Different from the conventional M-estimator, the proposed method effectively alleviates the reliance of excessive parameterization of M-estimator. Experiments are conducted in a typical urban area of Hong Kong with numerous dynamic objects, and the results show that the proposed method could effectively mitigate the effects of dynamic objects and improved accuracy of VINS is obtained when compared with the conventional method.

scholarly journals Conservative Sensor Error Modeling Using a Modified Paired Overbound Method and its Application in Satellite-Based Augmentation Systems

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2826
Author(s):  
Yan Zhang ◽  
Zhibin Xiao ◽  
Pengpeng Li ◽  
Xiaomei Tang ◽  
Gang Ou
Keyword(s):  
Interval Estimation ◽  
Original Method ◽  
Error Modeling ◽  
Maximum Deviation ◽  
Data Loss ◽  
Sensor Error ◽  
Confidence Interval Estimation ◽  
Integrity Risk ◽  
Non Gaussian ◽  
Improved Algorithm

Conservative sensor error modeling is of great significance in the field of safety-of-life. At present, the overbound method has been widely used in areas such as satellite-based augmentation systems (SBASs) and ground-based augmentation systems (GBASs) that provide integrity service. It can effectively solve the difficulties of non-Gaussian and non-zero mean error modeling and confidence interval estimation of user position error. However, there is still a problem in that the model is too conservative and leads to the lack of availability. In order to further improve the availability of SBASs, an improved paired overbound method is proposed in this paper. Compared with the traditional method, the improved algorithm no longer requires the overbound function to conform to the characteristics of the probability distribution function, so that under the premise of ensuring the integrity of the system, the real error characteristics can be more accurately modeled and measured. The experimental results show that the modified paired overbound method can improve the availability of the system with a probability of about 99%. In view of the fact that conservative error modeling is more sensitive to large deviations, this paper analyzes the robustness of the improved algorithm in the case of abnormal data loss. The maximum deviation under a certain integrity risk is used to illustrate the effectiveness of the improved paired overbound method compared with the original method.

scholarly journals Should the impact factor of the year of publication or the last available one be used when evaluating scientists?

2020 ◽  
Vol 18 (3) ◽  
pp. eM01
Author(s):  
Gustavo A. Slafer ◽  
Roxana Savin
Keyword(s):  
Impact Factor ◽  
Impact Factors ◽  
Independent Research ◽  
Common Procedure ◽  
Actual Impact ◽  
The Impact ◽  
First Time ◽  
The Relationship ◽  
Research Centres

Aim of study: A common procedure when evaluating scientists is considering the journal’s quartile of impact factors (within a category), many times considering the quartile in the year of publication instead of the last available ranking. We tested whether the extra work involved in considering the quartiles of each particular year is justifiedArea of study: EuropeMaterial and methods: we retrieved information from all papers published in 2008-2012 by researchers of AGROTECNIO, a centre focused in a range of agri-food subjects. Then, we validated the results observed for AGROTECNIO against five other European independent research centres: Technical University of Madrid (UPM) and the Universities of Nottingham (UK), Copenhagen (Denmark), Helsinki (Finland), and Bologna (Italy).Main results: The relationship between the actual impact of the papers and the impact factor quartile of a journal within its category was not clear, although for evaluations based on recently published papers there might not be much better indicators. We found unnecessary to determine the rank of the journal for the year of publication as the outcome of the evaluation using the last available rank was virtually the same.Research highlights: We confirmed that the journal quality reflects only vaguely the quality of the papers, and reported for the first time evidences that using the journal rank from the particular year that papers were published represents an unnecessary effort and therefore evaluation should be done simply considering the last available rank.

scholarly journals Fast Online Coordinate Correction of a Multi-Sensor for Object Identification in Autonomous Vehicles

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2006
Author(s):  
Wooyoung Lee ◽  
Minchul Lee ◽  
Myoungho Sunwoo ◽  
Kichun Jo
Keyword(s):  
Real Time ◽  
Error Estimation ◽  
Correction Method ◽  
Object Identification ◽  
Computational Effort ◽  
Error Modeling ◽  
Estimation Methods ◽  
Time Error ◽  
Sensor Error ◽  
Coordinate Correction

Multi-sensor perception systems may have mismatched coordinates between each sensor even if the sensor coordinates are converted to a common coordinate. This discrepancy can be due to the sensor noise, deformation of the sensor mount, and other factors. These mismatched coordinates can seriously affect the estimation of a distant object’s position and this error can result in problems with object identification. To overcome these problems, numerous coordinate correction methods have been studied to minimize coordinate mismatching, such as off-line sensor error modeling and real-time error estimation methods. The first approach, off-line sensor error modeling, cannot cope with the occurrence of a mismatched coordinate in real-time. The second approach, using real-time error estimation methods, has high computational complexity due to the singular value decomposition. Therefore, we present a fast online coordinate correction method based on a reduced sensor position error model with dominant parameters and estimate the parameters by using rapid math operations. By applying the fast coordinate correction method, we can reduce the computational effort within the necessary tolerance of the estimation error. By experiments, the computational effort was improved by up to 99.7% compared to the previous study, and regarding the object’s radar the identification problems were improved by 94.8%. We conclude that the proposed method provides sufficient correcting performance for autonomous driving applications when the multi-sensor coordinates are mismatched.

scholarly journals Image Superresolution Based on Locally Adaptive Mixed-Norm

2010 ◽  
Vol 2010 ◽  
pp. 1-4 ◽  
Author(s):  
Osama A. Omer ◽  
Toshihisa Tanaka
Keyword(s):  
Additive Noise ◽  
Great Influence ◽  
Super Resolution ◽  
Error Modeling ◽  
Mixed Norm ◽  
Registration Error ◽  
High Resolution Image ◽  
Locally Adaptive ◽  
Simulation Results

In a typical superresolution algorithm, fusion error modeling, including registration error and additive noise, has a great influence on the performance of the super-resolution algorithms. In this letter, we show that the quality of the reconstructed high-resolution image can be increased by exploiting proper model for the fusion error. To properly model the fusion error, we propose to minimize a cost function that consists of locally and adaptively weighted - and -norms considering the error model. Binary weights are used so as to adaptively select - or -norm, based on the local errors. Simulation results demonstrate that proposed algorithm can overcome disadvantages of using either - or -norm.

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