Orientation Uncertainty Research Articles

The descriptive analysis of depressive symptoms and White Blood Cell (WBC) count between the sexual minorities and heterosexual identifying individuals in a nationally representative sample: 2005–2014

BackgroundSexual minorities are at a higher risk of suffering from depressive symptoms compared with heterosexual individuals. Only a few studies have examined the conditions of having depressive symptoms within different sexual minority groups, especially people with sexual orientation uncertainty in a nationally representative sample. Furthermore, few studies have explored whether the mean white blood count (WBC) is different between people with and without depressive symptoms among different sexual minority groups in a nationally representative sample.MethodsWe analyzed the National Health and Nutrition Examination Survey (NHANES) data from 2005 to 2014 with a sample of 14,090 subjects. We compared the prevalence of depressive symptoms in subpopulations stratified by sex, sexual minority status, and race. We also examined the difference in mean WBC count between depressed and non-depressed people among heterosexual individuals and different sexual minority groups. Additionally, two multivariable logistic regression models were used to explore the association between sexual minority status and depressive symptoms, treating sexual minority status as both a binary and categorical variable.ResultsFemale sex (OR: 1.96, 95% CI: 1.72—2.22) and sexual minority status (OR: 1.79, 95% CI: 1.47—2.17) were both independently associated with depressive symptoms. Within the sexual minority population, subjects who were unsure about their sexual identities had the highest odds of having depressive symptoms (OR: 2.56, 95% CI: 1.40—4.68). In the subgroup analysis considering intersectionality, black sexual minority females had the highest rate of depressive symptoms (19.4%, 95% CI: 7.72—40.98). Finally, the mean WBC count differed significantly between people with and without depressive symptoms among male heterosexual individuals, female heterosexual individuals, and female sexual minorities, but not among male sexual minorities.ConclusionsBased on sex, race, and sexual minority status, black females of sexual minority status had the highest rate of depressive symptoms. Within sexual minority groups, participants who were unsure about their sexual identities had the highest odds of having depressive symptoms. Finally, the mean WBC count was significantly higher among people with depressive symptoms than those without depressive symptoms only among male heterosexuals, female heterosexuals, and female sexual minorities, but not among male sexual minorities. Future research should investigate the social and biological mechanisms of the differences.

Probe-Field-Ellipticity-Induced Shift in an Atomic Clock

We investigate near-resonant ac-Stark shifts for optical atomic clocks, which can also be interpreted as a special class of line-pulling effects due to the Zeeman structure of atomic levels split in a dc magnetic field. This shift can arise due to residual ellipticity in the polarization of the probe field and uncertainty in the magnetic field orientation. Such a shift can have an arbitrary sign and, for some experimental conditions, can reach a fractional value of the order of ${10}^{\ensuremath{-}18}$--${10}^{\ensuremath{-}19}$, i.e., it is not negligible. Thus, it should be taken into account in the uncertainty budgets for modern ultraprecise atomic clocks. In addition, it is shown that when using hyper-Ramsey spectroscopy, this shift can be reduced to a level much lower than ${10}^{\ensuremath{-}19}$.

Monte Carlo study of uncertainty propagation in Euler rotational kinematic equation

We perform a Monte Carlo simulation to study the propagation of uncertainty in Euler rotational kinematic equation. The rate gyro data from an IMU sensor are inserted into the Euler rotational kinematic differential equation to calculate the sensor orientation, which is specified by its Euler angles. The uncertainty of sensor orientation is determined by simulating the variation of rate gyro input due to its uncertainty obtained from the calibration process. We find that the uncertainties of the Euler angles grow with time. Their values are proportional to the square root of time elapse. We find also that the uncertainties are linearly proportional to the uncertainties of rate gyro data and the square root of sampling time. These results interestingly resemble the behaviour of the Wiener process, with the step size of random walk depends on the time step. We can use these results as a criterion to choose a suitable IMU due to the characteristic of the integrated rate gyro sensor, especially in the application like the vehicle tracking or posture warning for elderly/disable people, where the precise sensor orientation is needed for a long period of operation.

On the properties of residential rooftop azimuth and tilt uncertainties for photovoltaic power generation modeling and hosting capacity analysis

One of the essential epistemic uncertainties that has not yet been studied enough for distributed photovoltaic systems is the azimuth and tilt of rooftop photovoltaic panels, as previous studies of grid impacts and hosting capacity have tended to assume uniform and optimal roof facet conditions. In this study, rooftop facet orientation distributions are presented and analyzed for all single-family buildings in the Swedish city of Uppsala, based on LiDAR-based data that consist of every roof facet from the around 13,500 single-family buildings in the city. From these distributions, novel methods to proportionally include less suitable roofs for every penetration level are proposed using a simple method based on normal and uniform probability density functions, and are tested for both time-series and stochastic hosting capacity analysis. The results show that under the assumption that the best roof facets are utilized first, a uniform distribution for rooftop facet azimuth and a normal distribution for rooftop facet tilt with parameters that depend linearly on the penetration level were shown to be accurate. The hosting capacity simulations demonstrate how the proposed methods perform significantly better in estimating the photovoltaic hosting capacity than the more common simplified methods for both time-series and stochastic hosting capacity analysis. The proposed model could help distribution system operators as well as researchers in this area to model the rooftop facet orientation uncertainty better and improve the quality of aggregated photovoltaic generation models and hosting capacity analyses.

Single-Shot Global and Local Context Refinement Neural Network for Head Detection

Head detection is a fundamental task, and it plays an important role in many head-related problems. The difficulty in creating the local and global context in the face of significant lighting, orientation, and occlusion uncertainty, among other factors, still makes this task a remarkable challenge. To tackle these problems, this paper proposes an effective detector, the Context Refinement Network (CRN), that captures not only the refined global context but also the enhanced local context. We use simplified non-local (SNL) blocks at hierarchical features, which can successfully establish long-range dependencies between heads to improve the capability of building the global context. We suggest a multi-scale dilated convolutional module for the local context surrounding heads that extracts local context from various head characteristics. In comparison to other models, our method outperforms them on the Brainwash and the HollywoodHeads datasets.

Probabilistic seismic source inversion of the 1886 Charleston, South Carolina, earthquake from macroseismic evidence: A major updating

The source of the 1886 Charleston, South Carolina earthquake influences the computed seismic hazard of the Southeastern U.S. and thus impacts public policy and engineering practice. However, because the 1886 rupture predated seismic instruments, its source is highly uncertain. This study presents probabilistic seismic-source inversions of the Charleston earthquake from liquefaction evidence and historical intensity reports. Using the latest predictive models and a novel inversion approach, we seek to constrain the magnitude, location, and orientation of the 1886 rupture. Probability distributions of rupture magnitude are conditioned on both the “Woodstock Fault” – a commonly inferred source of the 1886 event – and on an unknown source, wherein the uncertainties of fault location and orientation are considered. These distributions are compared to the Mw6.7-Mw7.5 distribution adopted by the U.S. National Seismic Hazard Model Project (NSHMP). Collectively, the results do not provide strong support for the hypothesized Woodstock Fault. This is not to say the Woodstock Fault does not exist or could not have caused the 1886 rupture, but rather, that the position of the 1886 source model cannot be constrained by the data and models studied herein, given the large uncertainties inherent to each. While this is at odds with the underlying assumption of prior studies, the results nonetheless generally uphold the magnitude distribution adopted by the NSHMP. The largest uncertainties inherent to this distribution are identified and could be diminished in the future. Finally, we note that the inversion methodology used here is not specific to any region, or to certain types of evidence, but can be applied to any seismic zone and to any co-seismic response. This methodology allows for uncertainty to be accounted for in a more complete and transparent manner when inverting seismic source parameters from macroseismic data. Of course, any limitations, biases, or unmodeled uncertainties inherent to these data must be understood, and their implications acknowledged, as further discussed herein.

A Framework of Improving Human Demonstration Efficiency for Goal-Directed Robot Skill Learning

Robot learning from humans allows robots to automatically adjust to stochastic and dynamic environments by learning from nontechnical end user’s demonstrations, which is best known as robot programming by demonstration, robot learning from demonstration, apprenticeship learning, and imitation learning. Although most of those methods are probabilistic, and their performances intensively depend on the demonstrated data, measuring and evaluating human demonstrations are rarely investigated. A poorly demonstrated data set with useless prior knowledge or redundant demonstrations increases the complexity and time cost of robot learning. To solve these problems, a goal-directed robot skill learning framework named GPm-MOGP is presented. It 1) decides when and where to add a new demonstration by calculating the trajectory uncertainty; 2) determines which demonstration is useless or redundant by Kullback–Leibler (KL) divergence; 3) implements robot skill learning with a minimum number of demonstrations using a multioutput Gaussian process; and 4) learns orientation uncertainty and representation by combining logarithmic and exponential maps. The proposed framework significantly reduces the demonstrated effort of nontechnical end users who lack an understanding of how and what the robot learns during the demonstrating process. To evaluate the proposed framework, a pick-and-place experiment was designed with five unseen goals to verify the effectiveness of our methods. This experiment is well illustrated with two phases: 1) demonstration efficiency and 2) skill representation and reproduction. The results indicate an improvement of 60% in human demonstration efficiency, compared to common learning from demonstrations (LfD) applications that require at least ten demonstrations, and the robot average success rate of pick-and-place task reaches 85%.

An Algorithm with Iteration Uncertainty Eliminate Based on Geomagnetic Fingerprint under Mobile Edge Computing for Indoor Localization.

Indoor localization problems are difficult due to that the information, such as WLAN and GPS, cannot achieve enough precision for indoor issues. This paper presents a novel indoor localization algorithm, GeoLoc, with uncertainty eliminate based on fusion of acceleration, angular rate, and magnetic field sensor data. The algorithm can be deployed in edge devices to overcome the problems of insufficient computing resources and long delay caused by high complexity of location calculation. Firstly, the magnetic map is built and magnetic values are matched. Secondly, orientation updating and position selection are iteratively executed using the fusion data, which gradually reduce uncertainty of orientation. Then, we filter the trajectory from a path set. By gradually reducing uncertainty, GeoLoc can bring a high positioning precision and a smooth trajectory. In addition, this method has an advantage in that it does not rely on any infrastructure such as base stations and beacons. It solves the common problems regarding the non-uniqueness of the geomagnetic fingerprint and the deviation of the sensor measurement. The experimental results show that our algorithm achieves an accuracy of less than 2.5 m in indoor environment, and the positioning results are relatively stable. It meets the basic requirements of indoor location-based services (LBSs).

Generation Z's expectations of their leaders: A cross-cultural, multi-dimensional investigation of leadership styles.

Gen Z representatives from one Western and one Eastern European country were the subjects of an empirical study investigating leadership style preferences and effects of motivation and performance on leader preferences. Data from 131 Ukrainian and 157 Austrian Gen Z representatives were analyzed. Different dimensions of leadership were examined, including transactional/transformational leadership and production/employee/change orientation of leaders. The results show significant differences in expectations between cultures, and within different leadership styles. Generation Z representatives from collectivist cultures with high power distance, long-term orientation, and high uncertainty avoidance have higher expectations of their future leaders than those from individualistic countries with lower power distance, less long-term orientation, and low uncertainty avoidance. A higher level of work motivation leads to a higher relevance of transformational, transactional pecuniary, and transactional non-pecuniary leadership styles in a collectivist, uncertainty-avoiding country with high power distance (Ukraine), while there is no effect in an individualistic country with low uncertainty avoidance and low power distance (Austria). Also, higher levels of work motivation lead to more appreciation of employee / production / change orientation in Ukraine, but only to higher levels of employee orientation in Austria. High performers in Austria tend to accept transactional sanction-based leadership, while this is not the case in Ukraine. In general, generations that may be described as universally similar have different perceptions depending on the culture they grew up in. For business leaders, the study provides insights into Gen Z employees and their leadership expectations. Managers from different business sectors working with employees from different cultural backgrounds should be aware of their employees’ different expectations. Recruiters will eventually have a better understanding of which arguments regarding leadership their future employees prefer and can adjust their recruitment messages accordingly.

Seafloor depth controls seismograph orientation uncertainty

SUMMARY This study evaluates the seafloor ambient noise environment that varies with the water depth based on a correction analysis of the horizontal sensor orientation for ocean-bottom seismographs. As ocean-bottom seismographs are mainly deployed as ‘free-fall’ installations, we have no information on which direction a horizontal sensor faces at the seafloor. An accurate sensor orientation is crucial for data processing based on seismic wavefields. Among several seismological approaches that use passive sources to correct the horizontal sensor azimuth, the particle motion of teleseismic Rayleigh waves is widely used for broad-band ocean-bottom seismographs. We performed seafloor seismic observations in the Hyuga-nada region at the western end of the Nankai subduction zone and deployed broad-band and short-period seismographs. However, studies have yet to investigate whether orientation correction via the Rayleigh-wave polarization method is valid for short-period data. The results of the Rayleigh wave method from our campaign observation data showed that the estimation uncertainty of short-period sensor orientations increased with a decreasing water depth; we observed a transition depth for the uncertainty at 2200–2600 m. The measurement quality, or the cross-correlation coefficient between the radial and Hilbert-transformed vertical components, also decreased at depths shallower than 2000 m. Moreover, an analysis of the noise power spectral densities showed that ambient noise levels during long periods (>10 s) increased with decreasing depth. Infragravity waves controlled vertical long-period noise levels, while ocean currents dominated horizontal long-period noise; both of these reduced the Rayleigh-wave signals as a function of environmental noise. Infragravity waves also likely distorted the Rayleigh waveforms. Both mechanisms contributed to the sudden rise in orientation uncertainty and low measurement quality at shallow stations (i.e. <2000 m). We confirmed that the variation in orientation uncertainty with the water depth can be used as an index for the ambient noise environment of the seafloor.

Fast data-driven computation and intuitive visualization of fiber orientation uncertainty in 3D-polarized light imaging

In recent years, the microscopy technology referred to as Polarized Light Imaging (3D-PLI) has successfully been established to study the brain’s nerve fiber architecture at the micrometer scale. The myelinated axons of the nervous tissue introduce optical birefringence that can be used to contrast nerve fibers and their tracts from each other. Beyond the generation of contrast, 3D-PLI renders the estimation of local fiber orientations possible. To do so, unstained histological brain sections of 70 μm thickness cut at a cryo-microtome were scanned in a polarimetric setup using rotating polarizing filter elements while keeping the sample unmoved. To address the fundamental question of brain connectivity, i. e., revealing the detailed organizational principles of the brain’s intricate neural networks, the tracing of fiber structures across volumes has to be performed at the microscale. This requires a sound basis for describing the in-plane and out-of-plane orientations of each potential fiber (axis) in each voxel, including information about the confidence level (uncertainty) of the orientation estimates. By this means, complex fiber constellations, e. g., at the white matter to gray matter transition zones or brain regions with low myelination (i. e., low birefringence signal), as can be found in the cerebral cortex, become quantifiable in a reliable manner. Unfortunately, this uncertainty information comes with the high computational price of their underlying Monte-Carlo sampling methods and the lack of a proper visualization. In the presented work, we propose a supervised machine learning approach to estimate the uncertainty of the inferred model parameters. It is shown that the parameter uncertainties strongly correlate with simple, physically explainable features derived from the signal strength. After fitting these correlations using a small sub-sample of the data, the uncertainties can be predicted for the remaining data set with high precision. This reduces the required computation time by more than two orders of magnitude. Additionally, a new visualization of the derived three-dimensional nerve fiber information, including the orientation uncertainty based on ellipsoids, is introduced. This technique makes the derived orientation uncertainty information visually interpretable.

Reliability study on the fatigue life of film cooling blades in advanced aero-engine turbines: Neglected crystal orientation uncertainty in casting

The orientation uncertainty of single-crystal turbine blades is a neglected practical engineering problem, and previous research has paid little attention to it. The work innovatively presents the random distribution regularity of the spatial orientation formed in the casting of single crystal blades and its influence on the structural reliability, which establish a foundation for the orientation design of blades. In this work, the uncertain characteristics of the spatial orientation for single crystal cast blades are mathematically quantified. Meanwhile, based on the classical material constitutive and life equations, the elastic-plastic theoretical and fatigue life evaluation models related to orientation are deduced and firstly applied to turbine blade engineering analysis. The elastic-plastic stress distributions of single-crystal turbine blades considering thermal effects are obtained by the fluid-structure interaction simulation. The spatial orientation distribution characteristics is presented from analyzing hundreds of samples. Finally, the influence of the uncertain orientation on the low cycle fatigue life reliability and structural failure probability of single-crystal turbine blades are clarified for the first time by using the BP neural network and stepwise response method. Our results demonstrate that crystal orientation uncertainty, especially in long-life design, can cause a serious failure probability, proving the necessity of orientation control for single crystal turbine blades clearly.

University Students’ Sexual Identity Development as a Determinant of Sexual Self-Hood

Sexual identity development was explored as a predictor of sexual self-concept and erotophobia–erotophilia using a student sample. Sexual identity development was operationalized using Worthington et al.’s Measure of Sexual Identity Development, which is theoretically based on Marcia’s identity model. Based on Worthington et al.’s scale, there are four sexual identity development domains: commitment to one’s sexual identity, exploration of one’s sexual identity, sexual orientation uncertainty, and synthesis/integration of one’s sexual identity. Several of Snell’s Sexual Self-Concept scales (e.g., sexual depression, sexual self-efficacy) and Tromovitch’s Comfort with Sexuality scales (e.g., comfort talking about sexuality, comfort with the sexual activities of others) were used to define sexual self-concept and erotophobia–erotophilia, respectively. A sample of students enrolled in an introductory psychology course at a Canadian university completed these instruments. Results suggest that sexual identity development was strongly predictive of sexual self-concept scales, multivariately. Sexual identity development was also predictive of comfort with sexuality. Sexual self-concept and erotophobia–erotophilia were also related to each other. These relationships supported the idea that a positive sexual identity development process contributes to a favorable view of the sexual self. However, the study was cross-sectional and correlational, so conclusions about directionality are preliminary and causality cannot be inferred.

Hybrid Position and Orientation Estimation for Visible Light Systems in the Presence of Prior Information on the Orientation

Visible light communication (VLC) is seen as a potential access option for fifth-generation (5G) wireless communication (Wang <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2014) and (Ayyash <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2016) and beyond 5G (Strinati <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2019). A reliable VLC system benefits from an accurate estimate of the receiver’s position and orientation. In many cases, the orientation of the receiver is estimated with an external orientation estimation device. However, these devices generally suffer from drift and misalignment, causing an uncertainty in the orientation presented to the receiver. Hence, the external device can only provide a probability distribution of the orientation to the position estimator, which can be used as prior information for the position estimation. Since the orientation of a receiver greatly affects the performance of a visible light system, the orientation uncertainty will degrade the performance of standard positioning algorithms, implying it should be taken into account when designing a robust positioning algorithm. In this paper, we design an received signal strength (RSS)-based hybrid position and orientation estimation algorithm using the hybrid maximum likelihood (ML)/maximum <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> (MAP) (HyMM) principle for a multiple LEDs - multiple photodiodes (PDs) (MLMP) system to take into account the presence of prior information on the orientation. The proposed HyMM estimator is compared with three existing estimators, i.e., the simultaneous position and orientation (SPO) estimator, the misspecified maximum likelihood (MML) estimator and the first-order-approximation-based positioning algorithm, subject to the orientation uncertainty. Further, in order to analytically assess the performance of the proposed estimator, the theoretical lower bound on the mean squared error (MSE), i.e. the hybrid Cramér-Rao bound (HCRB) for HyMM is derived. Computer simulations show an asymptotic tightness between the performance of the estimator and its associated theoretical lower bound.

How Does Political Appointment Affect Innovation Ambidexterity: The Case of China’s Central SOEs

Does political appointment affect innovation ambidexterity? To answer the interesting question, we distinguish two types of political appointments: government and party; and find that these different types of appointments do affect firms’ exploitative or exploratory innovations, inducing appointees by respectively incentive systems. Furthermore, we consider that these effects stem from time orientation and risk uncertainty and add two moderators: agent tenure and industry dynamism. We test our framework with a panel data set of central state-owned enterprises in China from 2006 to 2016. We find that agents appointed by the government will be more likely to pursue exploitative rather than exploratory innovation than agents appointed by the party, particularly when agent tenure is short and industry dynamism is low. Our research contributes to the literature on the effects of political appointment, the time and risk orientation of political appointment, and the relationship between corporate political activities and innovation ambidexterity

Impact of color on polarization-based 3D imaging and countermeasures.

Diffuse polarization-based 3D imaging has flourished with the ability to obtain the 3D shapes of objects without multiple detectors, active mode lighting, or complex mechanical structures, which are major drawbacks of other methods for 3D imaging in natural scenes. However, traditional polarization-based 3D imaging technology introduces color distortion when reconstructing the surface of multi-colored targets. We propose a polarization-based 3D imaging model to recover the 3D geometry of multi-colored Lambertian objects. In particular, chromaticity-based color removal theory is used to restore the intrinsic intensity, which is modulated only by the target shape, and we apply the recovered intrinsic intensity to address the orientation uncertainty of target normals due to azimuth ambiguity. Finally, we integrate the corrected normals to reconstruct high-precision 3D shapes. Experimental results demonstrate that the proposed model has the ability to reconstruct multi-colored Lambertian objects exhibiting non-uniform reflectance from single views under natural light conditions.

Real-time 3D UAV Path Planning in Dynamic Environments with Uncertainty

The integration of Unmanned Aerial Vehicles (UAVs) is being proposed in a spectrum of applications varying from military to civil. In these applications, UAVs are required to safely navigate in real-time in dynamic and uncertain environments. Uncertainty can be present in both the UAV itself and the environment. Through a literature study, this paper first identifies, quantifies and models different uncertainty sources using bounding shapes. Then, the UAV model, path planner parameters and four scenarios of different complexity are defined. To investigate the effect of uncertainty on path planning performance, uncertainty in obstacle position and orientation and UAV position is varied between 2% and 20% for each uncertainty source first separately and then concurrently. Results show a deterioration in path planning performance with the inclusion of both uncertainty types for all scenarios for both A* and the Rapidly-Exploring Random Tree (RRT) algorithms, especially for RRT. Faster and shorter paths with similar same success rates (&gt;95%) result for the RRT algorithm with respect to the A* algorithm only for simple scenarios. The A* algorithm performs better than the RRT algorithm in complex scenarios.

The mechanism for SMEs growth by applying stochastic dynamical approach

The purpose of this paper is to establish an interpretable framework to describe the growth mechanism of small and micro enterprises (SMEs) under the tech-economic paradigm and develop a new measurement method for growth performance. Based on stochastic resonance theory, as the inherent capital–product switches are applied with capital driving and risk incentive, SMEs’ growth might exhibit an optimal state subject to the implementation of different strategies. Simulation results verify these strategies from management orientation, specialization level, and environmental uncertainty to reveal some nonintuitive phenomena. These findings lead us to create useful decision rules in risk management, growth path optimization or SME loans. Finally we wish the framework established in this paper may provide another new mathematical way to explain Schumpter’s economic development theory based on SMEs growth with innovation.

Phase-field based robust topology and orientation optimization under fiber orientation uncertainty

Recently, there has been an increased usage of fiber-reinforced composites in aerospace and automobile due to their celebrated mechanical properties. Thus, a massive research effort has hitherto been done to develop a method for concurrent optimization of structural topology and fiber orientation. Topology-optimized continuous fiber-reinforced structures tend to show complex morphologies with spatially varying fiber orientation. Due to this nature, it is nearly possible to manufacture the results only through additive manufacturing technology However, a gap must exist between prescribed optimized fiber orientation and fiber orientation of manufactured composites under a limited degree of precision of the manufacturing process. Robust anisotropic topology optimization under such uncertainty, therefore, needs to be developed. This presentation aims to present a novel robust topology optimization methodology for fiber-reinforced materials under fiber orientation uncertainty. Our method is built upon Phase-Field based topology optimization which is suitable for this type of optimization method. The effectiveness of our method is validated through the stiffness maximization problem and the fundamental eigenfrequency maximization problem.

Highly Accurate Pose Estimation as a Reference for Autonomous Vehicles in Near-Range Scenarios

To validate the accuracy and reliability of onboard sensors for object detection and localization for driver assistance, as well as autonomous driving applications under realistic conditions (indoors and outdoors), a novel tracking system is presented. This tracking system is developed to determine the position and orientation of a slow-moving vehicle during test maneuvers within a reference environment (e.g., car during parking maneuvers), independent of the onboard sensors. One requirement is a 6 degree of freedom (DoF) pose with position uncertainty below 5 mm (3σ), orientation uncertainty below 0.3° (3σ), at a frequency higher than 20 Hz, and with a latency smaller than 500 ms. To compare the results from the reference system with the vehicle’s onboard system, synchronization via a Precision Time Protocol (PTP) and system interoperability to a robot operating system (ROS) are achieved. The developed system combines motion capture cameras mounted in a 360° panorama view setup on the vehicle, measuring retroreflective markers distributed over the test site with known coordinates, while robotic total stations measure a prism on the vehicle. A point cloud of the test site serves as a digital twin of the environment, in which the movement of the vehicle is visualized. The results have shown that the fused measurements of these sensors complement each other, so that the accuracy requirements for the 6 DoF pose can be met while allowing a flexible installation in different environments.