Calibration method for underwater visual ground-truth system

Available methods for assessing hot-mix-asphalt pavements are typically restricted to destructive methods such as coring that damage the pavement and are limited in coverage. Recently, density profiling systems (DPS) have become available with the capability of measuring asphalt compaction continuously, giving instantaneous measurements a few hundred feet behind the final roller of the freshly placed pavement. Further developments of the methods involved with DPS processing have allowed for coreless calibration by correlating dielectric measurements with asphalt specimens fabricated at variable air void contents using superpave gyratory compaction. These developments make DPS technology an attractive potential tool for quality control because of the real-time nature of the results, and quality assurance because of the ability to measure a more statistically significant amount of data as compared with current quality assurance methods such as coring. To test the viability of these recently developed methods for implementation, multiple projects were selected for field trials. Each field trial was used to assess the coreless calibration prediction by comparing with field cores where dielectric measurements were made. Ground truth core validation on each project showed the reasonableness of the coreless calibration method. The validated dielectric to air void prediction curves allowed for assessment of the tested pavements in relation to as-built characteristics, with the DPS providing the equivalent of approximately 100,000 cores per mile. Statistical measures were used to demonstrate how DPS can provide a comprehensive asphalt compaction evaluation that can be used to inform construction-related decisions and has potential as a future quality assurance tool.

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Formation Control of Quadrotor UAVs by Vision-Based Positioning

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.798.282 ◽

2015 ◽

Vol 798 ◽

pp. 282-286

Author(s):

Wooh Yun Kim ◽

Ji Wook Kwon ◽

Ji Won Seo

Keyword(s):

Formation Control ◽

Control Method ◽

Ground Truth ◽

Calibration Method ◽

Dynamics Model ◽

The Third ◽

Position Data ◽

Quadrotor Uavs ◽

Quadrotor Unmanned Aerial Vehicles ◽

Visual Marker

In this paper, a formation control method of quadrotor Unmanned Aerial Vehicles (UAVs) by vision-based positioning is presented. The relative positions and attitudes of two UAVs with respect to a visual marker attached to the third UAV is estimated by a camera calibration method. Based on the estimated positions and attitudes, two UAVs are controlled to the desired positions to form a given formation with respect to the third UAV. A simplified dynamics model of a quadrotor UAV is utilized to design a controller. The proposed formation control method is validated by an experiment with a motion capture system which provides the ground truth of the position data.

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Calibration of Planar Reflectors Reshaping LiDAR’s Field of View

Sensors ◽

10.3390/s21196501 ◽

2021 ◽

Vol 21 (19) ◽

pp. 6501

Author(s):

Michał Pełka ◽

Janusz Będkowski

Keyword(s):

Open Source ◽

Mechanical Design ◽

Laser Scanner ◽

Measurement Instrument ◽

Ground Truth ◽

Calibration Method ◽

Field Of View ◽

Ground Truth Data ◽

Calibration Process ◽

Data Registration

This paper describes the calibration method for calculating parameters (position and orientation) of planar reflectors reshaping LiDAR’s (light detection and ranging) field of view. The calibration method is based on the reflection equation used in the ICP (Iterative Closest Point) optimization. A novel calibration process as the multi-view data registration scheme is proposed; therefore, the poses of the measurement instrument and parameters of planar reflectors are calculated simultaneously. The final metric measurement is more accurate compared with parameters retrieved from the mechanical design. Therefore, it is evident that the calibration process is required for affordable solutions where the mechanical design can differ from the inaccurate assembly. It is shown that the accuracy is less than 20 cm for almost all measurements preserving long-range capabilities. The experiment is performed based on Livox Mid-40 LiDAR augmented with six planar reflectors. The ground-truth data were collected using Z + F IMAGER 5010 3D Terrestrial Laser Scanner. The calibration method is independent of mechanical design and does not require any fiducial markers on the mirrors. This work fulfils the gap between rotating and Solid-State LiDARs since the field of view can be reshaped by planar reflectors, and the proposed method can preserve the metric accuracy. Thus, such discussion concludes the findings. We prepared an open-source project and provided all the necessary data for reproducing the experiments. That includes: Complete open-source code, the mechanical design of reflector assembly and the dataset which was used in this paper.

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Calibration of Short Range 2D Laser Range Finder for 3D SLAM Usage

Journal of Sensors ◽

10.1155/2016/3715129 ◽

2016 ◽

Vol 2016 ◽

pp. 1-13 ◽

Cited By ~ 8

Author(s):

Petr Olivka ◽

Michal Krumnikl ◽

Pavel Moravec ◽

David Seidl

Keyword(s):

Short Range ◽

Three Dimensional ◽

Ground Truth ◽

Calibration Method ◽

Calibration Procedure ◽

High Angular Resolution ◽

Laser Range Finder ◽

Range Finder ◽

Laser Range ◽

Practical Applications

The laser range finder is one of the most essential sensors in the field of robotics. The laser range finder provides an accurate range measurement with high angular resolution. However, the short range scanners require an additional calibration to achieve the abovementioned accuracy. The calibration procedure described in this work provides an estimation of the internal parameters of the laser range finder without requiring any special three-dimensional targets. This work presents the use of a short range URG-04LX scanner for mapping purposes and describes its calibration. The precision of the calibration was checked in an environment with known ground truth values and the results were statistically evaluated. The benefits of the calibration are also demonstrated in the practical applications involving the segmentation of the environment. The proposed calibration method is complex and detects all major manufacturing inaccuracies. The procedure is suitable for easy integration into the current manufacturing process.

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A Comparison of Simple Structure Rotation Criteria in Temporal Exploratory Factor Analysis for Event-Related Potential Data

Methodology ◽

10.1027/1614-2241/a000175 ◽

2019 ◽

Vol 15 (Supplement 1) ◽

pp. 43-60 ◽

Cited By ~ 3

Author(s):

Florian Scharf ◽

Steffen Nestler

Keyword(s):

Factor Analysis ◽

Exploratory Factor Analysis ◽

Simple Structure ◽

Ground Truth ◽

Event Related Potential ◽

Suitable Alternative ◽

Factor Rotation ◽

Rotation Methods ◽

Simple Structure Rotation ◽

Temporal Overlap

Abstract. It is challenging to apply exploratory factor analysis (EFA) to event-related potential (ERP) data because such data are characterized by substantial temporal overlap (i.e., large cross-loadings) between the factors, and, because researchers are typically interested in the results of subsequent analyses (e.g., experimental condition effects on the level of the factor scores). In this context, relatively small deviations in the estimated factor solution from the unknown ground truth may result in substantially biased estimates of condition effects (rotation bias). Thus, in order to apply EFA to ERP data researchers need rotation methods that are able to both recover perfect simple structure where it exists and to tolerate substantial cross-loadings between the factors where appropriate. We had two aims in the present paper. First, to extend previous research, we wanted to better understand the behavior of the rotation bias for typical ERP data. To this end, we compared the performance of a variety of factor rotation methods under conditions of varying amounts of temporal overlap between the factors. Second, we wanted to investigate whether the recently proposed component loss rotation is better able to decrease the bias than traditional simple structure rotation. The results showed that no single rotation method was generally superior across all conditions. Component loss rotation showed the best all-round performance across the investigated conditions. We conclude that Component loss rotation is a suitable alternative to simple structure rotation. We discuss this result in the light of recently proposed sparse factor analysis approaches.

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Brain FDG PET for the Etiological Diagnosis of Clinically Uncertain Cognitive Impairment During Delirium in Remission

Journal of Alzheimer s Disease ◽

10.3233/jad-200530 ◽

2020 ◽

Vol 77 (4) ◽

pp. 1609-1622

Author(s):

Franziska Mathies ◽

Catharina Lange ◽

Anja Mäurer ◽

Ivayla Apostolova ◽

Susanne Klutmann ◽

...

Keyword(s):

Cognitive Impairment ◽

Fdg Pet ◽

False Negative ◽

Ground Truth ◽

Etiological Diagnosis ◽

Geriatric Unit ◽

Positron Emission ◽

The Brain ◽

Hospitalized Geriatric Patients

Background: Positron emission tomography (PET) of the brain with 2-[F-18]-fluoro-2-deoxy-D-glucose (FDG) is widely used for the etiological diagnosis of clinically uncertain cognitive impairment (CUCI). Acute full-blown delirium can cause reversible alterations of FDG uptake that mimic neurodegenerative disease. Objective: This study tested whether delirium in remission affects the performance of FDG PET for differentiation between neurodegenerative and non-neurodegenerative etiology of CUCI. Methods: The study included 88 patients (82.0±5.7 y) with newly detected CUCI during hospitalization in a geriatric unit. Twenty-seven (31%) of the patients were diagnosed with delirium during their current hospital stay, which, however, at time of enrollment was in remission so that delirium was not considered the primary cause of the CUCI. Cases were categorized as neurodegenerative or non-neurodegenerative etiology based on visual inspection of FDG PET. The diagnosis at clinical follow-up after ≥12 months served as ground truth to evaluate the diagnostic performance of FDG PET. Results: FDG PET was categorized as neurodegenerative in 51 (58%) of the patients. Follow-up after 16±3 months was obtained in 68 (77%) of the patients. The clinical follow-up diagnosis confirmed the FDG PET-based categorization in 60 patients (88%, 4 false negative and 4 false positive cases with respect to detection of neurodegeneration). The fraction of correct PET-based categorization did not differ between patients with delirium in remission and patients without delirium (86% versus 89%, p = 0.666). Conclusion: Brain FDG PET is useful for the etiological diagnosis of CUCI in hospitalized geriatric patients, as well as in patients with delirium in remission.

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Construction of Subjective Vehicle Detection Evaluation Model Considering Shift from Ground Truth Position

IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences ◽

10.1587/transfun.e102.a.1246 ◽

2019 ◽

Vol E102.A (9) ◽

pp. 1246-1249 ◽

Cited By ~ 1

Author(s):

Naho ITO ◽

Most Shelina AKTAR ◽

Yuukou HORITA

Keyword(s):

Evaluation Model ◽

Vehicle Detection ◽

Ground Truth ◽

Detection Evaluation

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A Flying Gray Ball Multi-illuminant Image Dataset for Color Research

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2020.64.5.050411 ◽

2020 ◽

Vol 64 (5) ◽

pp. 50411-1-50411-8

Author(s):

Hoda Aghaei ◽

Brian Funt

Keyword(s):

Machine Vision ◽

Spectral Reflectance ◽

Color Constancy ◽

Ground Truth ◽

Practical Approach ◽

Post Processing ◽

Illumination Estimation ◽

Processing Step ◽

Image Dataset

Abstract For research in the field of illumination estimation and color constancy, there is a need for ground-truth measurement of the illumination color at many locations within multi-illuminant scenes. A practical approach to obtaining such ground-truth illumination data is presented here. The proposed method involves using a drone to carry a gray ball of known percent surface spectral reflectance throughout a scene while photographing it frequently during the flight using a calibrated camera. The captured images are then post-processed. In the post-processing step, machine vision techniques are used to detect the gray ball within each frame. The camera RGB of light reflected from the gray ball provides a measure of the illumination color at that location. In total, the dataset contains 30 scenes with 100 illumination measurements on average per scene. The dataset is available for download free of charge.

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Nanosecond Photodynamics Simulations of a Cis-Trans Isomerization Are Enabled by Machine Learning

10.26434/chemrxiv.13047863 ◽

2020 ◽

Author(s):

Jingbai Li ◽

Patrick Reiser ◽

André Eberhard ◽

Pascal Friederich ◽

Steven Lopez

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Excited State ◽

Adaptive Sampling ◽

Computational Cost ◽

Ground Truth ◽

Absolute Error ◽

Photochemical Reactions ◽

Computational Techniques ◽

Full Potential

Photochemical reactions are being increasingly used to construct complex molecular architectures with mild and straightforward reaction conditions. Computational techniques are increasingly important to understand the reactivities and chemoselectivities of photochemical isomerization reactions because they offer molecular bonding information along the excited-state(s) of photodynamics. These photodynamics simulations are resource-intensive and are typically limited to 1–10 picoseconds and 1,000 trajectories due to high computational cost. Most organic photochemical reactions have excited-state lifetimes exceeding 1 picosecond, which places them outside possible computational studies. Westermeyr et al. demonstrated that a machine learning approach could significantly lengthen photodynamics simulation times for a model system, methylenimmonium cation (CH2NH2+).We have developed a Python-based code, Python Rapid Artificial Intelligence Ab Initio Molecular Dynamics (PyRAI2MD), to accomplish the unprecedented 10 ns cis-trans photodynamics of trans-hexafluoro-2-butene (CF3–CH=CH–CF3) in 3.5 days. The same simulation would take approximately 58 years with ground-truth multiconfigurational dynamics. We proposed an innovative scheme combining Wigner sampling, geometrical interpolations, and short-time quantum chemical trajectories to effectively sample the initial data, facilitating the adaptive sampling to generate an informative and data-efficient training set with 6,232 data points. Our neural networks achieved chemical accuracy (mean absolute error of 0.032 eV). Our 4,814 trajectories reproduced the S1 half-life (60.5 fs), the photochemical product ratio (trans: cis = 2.3: 1), and autonomously discovered a pathway towards a carbene. The neural networks have also shown the capability of generalizing the full potential energy surface with chemically incomplete data (trans → cis but not cis → trans pathways) that may offer future automated photochemical reaction discoveries.

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