Sensor Position Research Articles

Urban Visual Localization of Block-Wise Monocular Images with Google Street Views

Urban visual localization is the process of determining the pose (position and attitude) of the imaging sensor (or platform) with the help of existing geo-referenced data. This task is critical and challenging for many applications, such as autonomous navigation, virtual and augmented reality, and robotics, due to the dynamic and complex nature of urban environments that may obstruct Global Navigation Satellite Systems (GNSS) signals. This paper proposes a block-wise matching strategy for urban visual localization by using geo-referenced Google Street View (GSV) panoramas as the database. To determine the pose of the monocular query images collected from a moving vehicle, neighboring GSVs should be found to establish the correspondence through image-wise and block-wise matching. First, each query image is semantically segmented and a template containing all permanent objects is generated. The template is then utilized in conjunction with a template matching approach to identify the corresponding patch from each GSV image within the database. Through the conversion of the query template and corresponding GSV patch into feature vectors, their image-wise similarity is computed pairwise. To ensure reliable matching, the query images are temporally grouped into query blocks, while the GSV images are spatially organized into GSV blocks. By using the previously computed image-wise similarities, we calculate a block-wise similarity for each query block with respect to every GSV block. A query block and its corresponding GSV blocks of top-ranked similarities are then input into a photogrammetric triangulation or structure from motion process to determine the pose of every image in the query block. A total of three datasets, consisting of two public ones and one newly collected on the Purdue campus, are utilized to demonstrate the performance of the proposed method. It is shown it can achieve a meter-level positioning accuracy and is robust to changes in acquisition conditions, such as image resolution, scene complexity, and the time of day.

Research on the technology of intelligent vehicle sensor positioning system in autonomous driving

In this comprehensive exploration of positioning and navigation technologies, we have delved into the intricacies of GPS (Global Positioning System), IMU (Inertial Measurement Unit), and VPS (Visual Positioning Systems) systems, shedding light on their unique attributes and applications. GPS, a global satellite-based system, offers precise positioning on a worldwide scale, although it encounters challenges in complex urban and environmental conditions. Its role in navigation and tracking is undeniable. IMU, characterized by accelerometers and gyroscopes, excels in delivering high-precision positioning over short durations, making it invaluable for applications in aviation, robotics, and virtual reality. However, it is susceptible to drift over time. Visual Positioning Systems (VPS), harnessing computer vision and visual sensors, provide remarkable sub-meter to centimeter-level accuracy when conditions are optimal. Their significance is particularly pronounced in indoor navigation, augmented reality, and robotics, although they may face challenges in less favorable environments. These technologies are not isolated but can synergize to enhance accuracy and reliability. GPS and IMU collaborate to compensate for signal disruptions, while GPS and VPS join forces to tackle urban complexities. IMU and VPS integration offer precise indoor navigation and augmented reality experiences, delivering impeccable positioning and orientation data. Ultimately, the choice of technology hinges on specific application requirements, encompassing accuracy, environmental considerations, cost factors, and the need for complementary systems. As these technologies advance, they hold the promise of revolutionizing navigation across various domains, from autonomous vehicles to immersive augmented reality environments.

The Automatic Algorithm of Optimizing the Position of Structured Light Sensors

Optical 3D detection technology has a wide range of applications in industrial detection, agricultural production, and so on. Its advantages are non-contact, efficiency, and high precision. However, the specular reflection problem affects model coverage and measurement accuracy. An optimization algorithm for calculating the number and pose of sensors has been proposed to address this issue. First, the specular reflection problem is viewed as a multi-sensor position search problem. Then, an optimization algorithm is used to find the optimal number and bit positions of sensors to avoid specular reflection. The experiment shows that the optimization results of this algorithm can cover the area to be measured with the least number of sensor combinations while avoiding the influence of specular reflection.

Comparative Evaluation of LiDAR systems for transport infrastructure: case studies and performance analysis

ABSTRACT Mobile laser scanners are vital for intelligent transport infrastructure, capturing detailed 3D road representations, but their accuracy depends on factors like sensor positioning and environment. This study compares two van-mounted Mobile Laser Scanners (MLS): the dual head Lynx Mobile Mapper and the single head VUX-1 HA, along with the terrestrial laser scanner Faro Focus XX30. Using point cloud reference data from Faro Focus XX30 and GNSS data from Trimble R8, performance is assessed in road, urban, and semi-urban environments. Accuracy is measured by the difference between Trimble GNSS and MLS coordinates. Geometric features of each LiDAR are compared, and mapping tasks in road and urban areas are performed using a machine learning classifier. Results show the MLS-single head scanner achieves satisfactory accuracy in roads and semi-urban areas, while Faro performs better in urban settings for classification. MLS-single head excels in road environments, while Faro is superior in urban ones. This analysis aids researchers and professionals in selecting the appropriate mobile laser scanner for mapping transport infrastructure, providing valuable insights into MLS systems’ comparative performance across different environments.

Thermal relaxation parameters estimation of non-Fourier heat transfer in laser-irradiated living tissue using the Levenberg-Marquardt algorithm

The present work is concerned with solving the inverse heat transfer problem (IHTP) to estimate the thermal relaxation parameters corresponding to the dual phase lag (DPL) model-based bio-heat transfer in laser-irradiated living tissue. This problem is divided into two parts: direct and inverse problems. The solution of the inverse problem has been obtained using the Levenberg–Marquardt algorithm. On the other hand, the solution to the direct problem is obtained by solving the DPL model-based bio-heat transfer equation (BHTE) with the transient radiative transfer equation (TRTE). So, the TRTE has been numerically solved by the modified discrete ordinate method (DOM) to find the intensity field in the living tissue subjected to short-pulsed laser radiation, and it acts as a heat source in the DPL model-based BHTE. So, the DPL model-based BHTE has been analytically solved by employing the finite integral transform technique to get the temperature variation inside the laser-irradiated living tissue. First, the computer code developed for estimating unknown thermal relaxation parameters using the Levenberg–Marquardt algorithm has been verified against the data given in the literature, and found good agreement between them. Then, the thermal response of laser-irradiated living tissue has been investigated. After that, the influence of sensor position on the sensitivity of the IHTP solution has been discussed. The impact of the total transient readings and the measurement error on estimating the unknown parameters have been investigated. The present study’s findings can significantly contribute to various parameter estimation problems where the conventional methods for direct measurements of parameters are impossible or very difficult. Furthermore, the current results may considerably impact the study of the thermic response of living tissue during laser-based photothermal therapy.

Accuracy of instrument tip position using fiber optic shape sensing for navigated bronchoscopy

The purpose of this study was to evaluate the accuracy of a method for estimating the tip position of a fiber optic shape-sensing (FOSS) integrated instrument being inserted through a bronchoscope. A modified guidewire with a multicore optical fiber was inserted into the working channel of a custom-made catheter with three electromagnetic (EM) sensors. The displacement between the instruments was manually set, and a point-based method was applied to match the position of the EM sensors to corresponding points on the shape. The accuracy was evaluated in a realistic bronchial model. An additional EM sensor was used to sample the tip of the guidewire, and the absolute deviation between this position and the estimated tip position was calculated. For small displacements between the tip of the FOSS integrated tool and the catheter, the median deviation in estimated tip position was ≤5 mm. For larger displacements, deviations exceeding 10 mm were observed. The deviations increased when the shape sensor had sharp curvatures relative to more straight shapes. The method works well for clinically relevant displacements of a biopsy tool from the bronchoscope tip, and when the path to the lesion has limited curvatures. However, improvements must be made to our configuration before pursuing further clinical testing.

Three-Way k-Means Model: Dynamic Optimal Sensor Placement for Efficient Environment Monitoring in Pig House

Sensors were of paramount importance in the context of poultry and livestock farming, serving as essential tools for monitoring a variety of production management parameters. The effective surveillance and optimal control of the swine facility environment critically depend on the implementation of a robust strategy for situating the optimal number of sensors in precisely the right locations. This study presents a dynamic sensor placement approach for pigsties using the three-way k-means algorithm. The method involves determining candidate sensor combinations through the application of the k-means algorithm and a re-clustering strategy. The optimal sensor locations were then identified using the Joint Entropy-Based Method (JEBM). This approach adjusts sensor positions based on different seasons (summer and winter) to effectively monitor the overall environment of the pigsty. We employ two clustering models, one based on particle swarm optimization and the other on genetic algorithms, along with a re-clustering strategy to identify candidate sensor combinations. The joint entropy-based method (JEBM) helps select the optimal sensor placement. Fused data from the optimal sensor layout undergo a fuzzy fusion process, reducing errors compared to direct averaging. The results show varying sensor needs across seasons, and dynamic placement enhances pigsty environment monitoring. Our approach reduced the number of sensors from 30 to 5 (in summer) and 6 (in winter). The optimal sensor positions for both seasons were integrated. Comparing the selected sensor layout to the average of all sensor readings representing the overall pigsty environment, the RMSE were 0.227–0.294 and the MAPE were 0.172–0.228, respectively, demonstrating the effectiveness of the sensor layout.

Laser-Structured Thin Film Sensor Technology for Sliding Bearings in Internal Combustion Engines

Abstract Global issues such as climate change, environmental pollution, and conservation of resources require manufacturers of internal combustion engines (ICE) to achieve substantially reduced emissions of greenhouse gases and pollutants as well as increased engine efficiency and durability. Condition monitoring and predictive maintenance approaches for sliding bearings in ICEs are key tools for increasing engine durability and saving resources by exploiting more of the useful lifetime of a bearing while avoiding critical engine operation caused by bearing wear and failure. These approaches require appropriate measurement technology capable of acquiring informative parameters that reflect the current condition of the bearings while withstanding the high temperatures and mechanical loads encountered inside the engine and accommodating spatial constraints. This paper deals with research work related to advanced sensor technology that is highly integrated into sliding bearings so that information is obtained nearly directly from relevant areas such as the bearing running layer and the lubrication gap. An isolated, sputtered sensor layer with a thickness of a few micrometers is employed in combination with a laser structuring process to form the desired thin film sensor structure below the bearing running surface. While several measurement parameters and corresponding sensor types are conceivable, this paper focuses on temperature and strain measurements that rely on a change in the electrical resistance of the sensor layer material. Promising sensor layouts and positions targeted for use in condition monitoring applications in ICEs are elaborated in detail. Developments and challenges in implementing the sensor technology concept—in particular with regard to the process of manufacturing the sensor as well as the wire contacting—are outlined in depth. The paper concludes by presenting measurement results obtained with this sensor technology at lab scale as well as an outlook toward implementing the instrumented bearings in ICEs.

Research on Real-Time Calculation Method of Transformer Temperature Field Based on Reduced Order Model and Sparse Measurement

The lifespan of transformers is closely related to their operating temperature. Among the existing temperature acquisition methods, the measurement method cannot obtain the complete temperature field temperature, while the numerical calculation method presents the challenge of significant computational expenses. This study theoretically derived how to determine the real-time temperature distribution of transformer windings based on sparse measurement data and reduced order models and solved sensor layout issues, including the number and placement position of sensors. In this study, the maximum calculation error of the temperature distribution is 1.8 K, and the calculation time is 0.38 seconds. The research findings suggest that multiple modes can contribute to the temperature distribution of a transformer. Under a constant load condition, the calculation of transformer temperature can be achieved by utilizing a temperature sensor positioned at the hot spot in conjunction with the first mode.

Effect of sensor position on the measurement of acoustic wave produced by partial discharges

This paper presents the finite element method (FEM) simulation of the propagation, measurement and evaluation of the time of arrival (TOA) of the acoustic wave created by a partial discharge (PD) in a transformer model using COMSOL multiphysics software. This model is a flat tank filled with an insulating liquid. In addition, 8 acoustic probes placed on one of the outer faces of the tank provide information on acoustic pressure levels for specific values of angles of incidence of the acoustic signal. The addition of signal transmission zones for each of the probes makes it possible to define precise paths for the acoustic signal, enabling the TOA of the acoustic wave to be evaluated for each path. The results of this study show that for angular values less than 40°, the error on the TOA is practically zero, but for values greater than 40° this error increases exponentially with the angle. This means that for an angle of 40.41° the error is 6μs, corresponding to 1.7%, and for an angle of 71.70° the error is 332μs, corresponding to 40.3%. This highlights the optimal nature of the choice of sensor position for locating partial discharge.

A Novel Approach to Light Detection and Ranging Sensor Placement for Autonomous Driving Vehicles Using Deep Deterministic Policy Gradient Algorithm

<div>This article presents a novel approach to optimize the placement of light detection and ranging (LiDAR) sensors in autonomous driving vehicles using machine learning. As autonomous driving technology advances, LiDAR sensors play a crucial role in providing accurate collision data for environmental perception. The proposed method employs the deep deterministic policy gradient (DDPG) algorithm, which takes the vehicle’s surface geometry as input and generates optimized 3D sensor positions with predicted high visibility. Through extensive experiments on various vehicle shapes and a rectangular cuboid, the effectiveness and adaptability of the proposed method are demonstrated. Importantly, the trained network can efficiently evaluate new vehicle shapes without the need for re-optimization, representing a significant improvement over classical methods such as genetic algorithms. By leveraging machine learning techniques, this research streamlines the sensor placement optimization process, enhancing the perception capabilities of autonomous driving vehicles. The optimized sensor configurations obtained from the DDPG algorithm lead to safer and more reliable autonomous driving systems, contributing to the advancement and widespread adoption of autonomous driving technology.</div>

The influence of the da Vinci surgical robot on electromagnetic tracking in a clinical environment

Robot-assisted surgery is increasingly used in surgery for cancer. Reduced overview and loss of anatomical orientation are challenges that might be solved with image-guided surgical navigation using electromagnetic tracking (EMT). However, the robot’s presence may distort the electromagnetic field, affecting EMT accuracy. The aim of this study was to evaluate the robot’s influence on EMT accuracy. For this purpose, two different electromagnetic field generators were used inside a clinical surgical environment: a table top field generator (TTFG) and a planar field generator (PFG). The position and orientation of sensors within the electromagnetic field were measured using an accurate in-house developed 3D board. Baseline accuracy was measured without the robot, followed by stepwise introduction of potential distortion sources (robot and robotic instruments). The absolute accuracy was determined within the entire 3D board and in the clinical working volume. For the baseline setup, median errors in the entire tracking volume within the 3D board were 0.9 mm and 0.3° (TTFG), and 1.1 mm and 0.4° (PFG). Adding the robot and instruments did not affect the TTFG’s position accuracy (p = 0.60), while the PFG’s accuracies decreased to 1.5 mm and 0.7° (p < 0.001). For both field generators, when adding robot and instruments, accuracies inside the clinical working volume were higher compared to the entire tracking 3D board volume, 0.7 mm and 0.3° (TTFG), and 1.1 mm and 0.7° (PFG). Introduction of a surgical robot and robotic instruments shows limited distortion of the EMT field, allowing sufficient accuracy for surgical navigation in robotic procedures.

Numerical Analysis for Appropriate Positioning of Ferrous Wear Debris Sensors with Permanent Magnet in Gearbox Systems

Numerical Analysis for Appropriate Positioning of Ferrous Wear Debris Sensors with Permanent Magnet in Gearbox Systems

Dynamic plastic hinge rotation measurement for RC columns under seismic loads using MEMS inclinometers

Plastic hinge rotation is an efficient and essential response parameter for post-disaster damage assessment on RC structures. In this paper, a micro-electronic mechanical system (MEMS) inclinometer-based approach is developed to dynamically measure the plastic hinge rotation of RC columns under earthquake loads. The plastic hinge rotation is regarded as the relative angle of rotation, measured by inclinometers, between cross-sections such that influence from the absolute rotation of floors can be eliminated. Meanwhile, since the inclinometers are densely installed near the bottom, translational acceleration-induced error can be compensated by subtracting the lowest sensor’s output from others’. However, issues caused by rotational and translational vibration are considered as the major challenges in real applications. The contribution of external vibration to the sensor’s overall output is revealed via analysis on a simplified inclinometer model. A numerical study utilizing typical RC column’s deformation characteristics and dimensions is conducted to analyze the influence of major factors: vibrational frequency, sensor position and damage extent. A method for static and dynamic inclinometer calibration is presented. The related calibration experiments are carried out to quantify the sensor’s sensitivity as well as the relationship between rotational frequency and error. The calibration method is validated with a set of vibration tests that use three seismic waves as input. Finally, performance of the proposed approach in full scale structures is investigated through a shake table test.

Development of a test bench for ventilation masks through 2D and 3D printing processes

ObjectiveMask choice is a key parameter in adaptation of ventilation through continuous positive airway pressure i.e. sleep apnea syndrome golden standard treatment. Two parameters of mask poor tolerance are cutaneous over pressure and unintentional leaks. The aim of this study was to create a test bench for masks to compare and localize cutaneous pressure areas, while monitoring leak flow. MethodsThe test bench is made of an artificial head on which customs-made pressure sensors were placed. The artificial head and the sensors were developed with various additive manufacturing technologies. Sensors measured electrical capacity variations (%VC) positively correlated with cutaneous pressure. For 4 different masks, different harness tightening conditions were tested. A flowmeter was used to monitor total leaks. ResultsWhen displayed on the nasal bridge of the artificial head, sensors detected tightening conditions: average of + 2%VC when harness was tight. This increase varied according to masks [1; 3]%VC and the sensors position [1.3; 2.2]%VC. ConclusionThis bench test allowed us to compare masks between them. Indeed it allowed us to localize and compare high pressure areas according to the mask. These data could be helpful for physicians and health professionals in the choice of the mask and for manufacturers in the development process

A Hybrid Traffic Sensor Deployment Model with Communication Consideration for Highways

This paper mainly studies the deployment of hybrid sensors on highways. By constructing the deployment location constraint model, the overall accuracy of sensor deployment can be maximized. In addition, in order to meet the needs of intelligent transportation, the consideration of traffic data communication is added to the work. The highway under study is first divided into several units, and the combination type of sensors is used to represent the possible layout of two adjacent sensors. Then, a 0–1 optimization model reflecting the interaction between the sensor position and the server position is established. Then, a two-step search algorithm is proposed to find the optimal solution of the model and determine the deployment scheme with maximum accuracy. Finally, an example is given to verify the method. The results show that there are significant differences between uniform unit-deployment schemes and non-uniform unit-deployment schemes. Through the sensitivity analysis of each factor, the influence of budget and communication radius on the deployment plan is proven. In addition, the ramp length can also have a negative impact on the target value.

Joint optimization of seismometer arrays for the cancellation of Newtonian noise from seismic body waves in the Einstein Telescope

Seismic Newtonian noise (NN) is predicted to limit the sensitivity of the Einstein Telescope (ET). It can be reduced with coherent noise cancellation techniques using data from seismometers. To achieve the best results, it is important to place the seismic sensors in optimal positions. A preliminary study on this topic was conducted for the ET: it focused on the optimization of the seismic array for the cancellation of NN at an isolated test mass (TM). In this paper, we expand the study to include the nested shape of ET, i.e. four TMs of the low-frequency interferometers at each vertex of the detector. Results are investigated in function of the polarization content of the seismic field composed of body waves. The study also examines how performance can be affected by displacing the sensor array from its optimal position or by operating at frequencies other than those used for optimization.

Ultra‐high frequency sensors positioning on the power transformer to mitigate the negative effects on the partial discharge localization accuracy

AbstractThis paper proposes a new method for improving the partial discharge (PD) localization accuracy by detecting the best places of ultra‐high frequency (UHF) sensors on the power transformer tank. In this way, firstly, the critical sites inside the power transformer that can produce PD are determined. Then the sensors’ positioning is done in such a way that if the PD happens at each point of these critical sites, the distance between the estimated PD location obtained by solving the time difference of arrival (TDOA) equations and the actual PD location is laid in an acceptable range. For this purpose, the TDOA equations are linearized around the critical PD coordinates as a matrix form A.X = B. The configuration of the sensor positions that minimizes the condition number of the matrix A is the best configuration. To evaluate the effectiveness of the proposed method, several simulations and evaluations are performed on a power transformer in the CST software. The obtained results and comparing them with the previous method and also conventional arbitrary sensors positioning confirm the effectiveness of this method.

Mapping palatal shape to electromagnetic articulography data: An approach using 3D scanning and sensor matching.

A method for superimposing the shape of the palate on three-dimensional (3D) electromagnetic articulography (EMA) data is proposed. A biteplate with a dental impression tray and EMA sensors is used to obtain the palatal shape and record the sensor positions. The biteplate is then 3D scanned, and the scanned palate is mapped to the EMA data by matching the sensor positions on the scanned image with those in the EMA readings. The average distance between the mapped palate and the EMA palate traces is roughly 1 mm for nine speakers and is comparable to the measurement error of the EMA.

PBF-LB of CuZn42 – Process development and material analysis

With the freedom of design and materials, laser-based powder bed fusion (PBF-LB) has become increasingly popular to improve and tailor gas flow, heat exchangers, and sensor positioning in complex assemblies. With the application of a heat-flux-burner for measuring the laminar burning velocities of hydrogen-based gases in mind, the fabrication of brass (CuZn42) by PBF-LB has been investigated in this paper. With little known research, this paper aims to do foundational work for the PBF-LB processing of this material. The feasibility was demonstrated for two different machines and empirical process models were set up to investigate the transferability of results. The relative density of cubes of different sizes and thin walls was optimized to > 99.5 % (max. 99.8 %) and demonstrated a maximum hardness of 136 HV 5. Therefore, this work demonstrated the feasibility of PBF-LB processing of brass for complex applications.