Indoor Geometry Research Articles

A GPU-accelerated computational fluid dynamics solver for assessing shear-driven indoor airflow and virus transmission by scale-resolved simulations

We explore the applicability of MATLAB for 3D computational fluid dynamics (CFD) of shear-driven indoor airflows. A new scale-resolving, large-eddy simulation (LES) solver titled DNSLABIB is proposed for MATLAB utilizing graphics processing units (GPUs). In DNSLABIB, the finite difference method is applied for the convection and diffusion terms while a Poisson equation solver based on the fast Fourier transform (FFT) is employed for the pressure. The immersed boundary method (IBM) for Cartesian grids is proposed to model solid walls and objects, doorways, and air ducts by binary masking of the solid/fluid domains. The solver is validated in two canonical reference cases and against experimental data. Then, we demonstrate the validity of DNSLABIB in a room geometry by comparing the results against another CFD software (OpenFOAM). Next, we demonstrate the solver performance in several isothermal indoor ventilation configurations and the implications of the results are discussed in the context of airborne transmission of COVID-19. The novel numerical findings using the new CFD solver are as follows. First, a linear scaling of DNSLABIB is demonstrated and a speed-up by a factor of 3-4 is also noted in comparison to similar OpenFOAM simulations. Second, ventilation in three different indoor geometries are studied at both low (0.1 m/s) and high (1 m/s) airflow rates corresponding to Re=5000 and Re=50000. An analysis of the indoor CO2 concentration is carried out as the room is emptied from stale, high CO2 content air. We estimate the air changes per hour (ACH) values for three different room geometries and show that the numerical estimates from 3D CFD simulations differ by 80%–150% (Re=50000) and 75%–140% (Re=5000) from the theoretical ACH value based on the perfect mixing assumption. Third, the analysis of the CO2 probability distributions (PDFs) indicates a relatively non-uniform distribution of fresh air indoors. Fourth, utilizing a time-dependent Wells-Riley analysis, an example is provided on the growth of the cumulative infection risk which is shown to reduce rapidly after the ventilation is started. The average infection risk is shown to reduce by a factor of 2 for lower ventilation rates (ACH=3.4-6.3) and 10 for the higher ventilation rates (ACH=37-64). Finally, we utilize the new solver to comment on respiratory particle transport indoors. A key contribution of the paper is to provide an efficient, GPU compatible CFD solver environment enabling scale-resolved simulations (LES/DNS) of airflow in large indoor geometries on a single GPU designed for high-performance computing. The demonstrated efficacy of MATLAB for GPU computing indicates a high potential of DNSLABIB for various future developments on airflow prediction.

DETECTION OF HIDDEN EDGES AND CORNERS IN SLAM-BASED INDOOR POINT CLOUDS

Abstract. Mobile mapping systems are commonly used for surveying buildings. The acquisition of the buildings’ indoor spaces with laser scanning or photogrammetry generates data in the form of point clouds. These point clouds are often used to create a model of those buildings, but so far with a low degree of automation. To automate this process, it is important to extract geometric information about corners, edges, and planes from unorganized indoor point clouds. In an indoor scenario consisting of several rooms including furniture and other objects, a point cloud is expected to show occlusions. Therefore, the detection of hidden corners and edges is of importance. In this work one approach based on contour point clouds and one approach based on planes are examined for the detection of corners and edges. Both approaches use RANSAC to extract either straight lines or planes. Through their intersection, edges and corners are determined. To examine the influence of the data quality on the results, the approaches are applied to and evaluated on different datasets of the same area of a building, which are captured by various measurement methods, including mobile mapping systems and terrestrial laser scanning. Therefore, we are creating a ground truth for parts of the building to evaluate the completeness and correctness of the corner detection. The approach based on planes presents itself to be more reliable in noisy and incomplete point clouds. The approach based on contour point clouds indicates advantages in terms of the complexity of a building´s indoor geometry.

Novel Robust Indoor Device-Free Moving-Object Localization and Tracking Using Machine Learning With Kalman Filter and Smoother

A novel robust device-free moving-object localization and tracking approach incorporating a machine-learning classifier with both Kalman filter and Kalman smoother is proposed in this work. The received signal-strength indicator (RSSI) maps for an arbitrary indoor geometry are simulated by the prevalent Feko channel-propagation emulator. Such instantaneous RSSIs of WiFi signals collected by different receivers indoors are acquired as a feature vector. The indoor geometry is first partitioned into a number of equisized zones, and then the underlying localization problem can be converted to the multiclassification problem. The RSSI feature vectors are collected as the training data for the gradient boosting decision-tree classifier. We propose a novel online tracking and offline localization approach using the advanced machine-learning technique regularized by Kalman filter and Kalman smoother, respectively. Simulation results demonstrate that our proposed new localization and tracking approach leads to the highest localization-accuracy of 73.1% over four test trajectories as the room is partitioned into 16 equisized zones. Moreover, our proposed new method also reaches the smallest average Euclidean-distance error of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$1.33{\kern0.99594pt}\text{m}$</tex-math></inline-formula> . Our proposed novel indoor localization and tracking scheme would be very promising and convenient as it can well serve for devices free indoor surveillance in the future.

EM DeepRay: An Expedient, Generalizable, and Realistic Data-Driven Indoor Propagation Model

Efficient and realistic indoor radio propagation modeling tools are inextricably intertwined with the design and operation of next-generation wireless networks. Machine-learning (ML)-based radio propagation models can be trained with simulated or real-world data to provide accurate estimates of wireless channel characteristics in a computationally efficient way. However, most of the existing research works on the ML-based propagation models focus on outdoor propagation modeling, while indoor data-driven propagation models remain site-specific with limited scalability. In this article, we present an efficient and credible ML-based radio propagation modeling framework for indoor environments. Specifically, we demonstrate how a convolutional encoder–decoder can be trained to replicate the results of a ray tracer, by encoding physics-based information of an indoor environment, such as the permittivity of the walls, and decoding it as the path loss (PL) heatmap for an environment of interest. Our model is trained over multiple indoor geometries and frequency bands, and it can eventually predict the PL for unknown indoor geometries and frequency bands within a few milliseconds. In addition, we illustrate how the concept of transfer learning can be leveraged to calibrate our model by adjusting its preestimate weights, allowing it to make predictions that are consistent with measurement data.

Novel Sensor/Access-Point Coverage-Area Maximization for Arbitrary Indoor Polygonal Geometries

Nowadays, sensor/access-point coverage is an essential problem for wireless communication and sensor systems, which will significantly impact the quality of access, monitoring, and surveillance. Indoor sensor/access-point placement still remains very challenging as the regions of interest (ROIs) or underlying geometries may be in an arbitrary polygonal shape. In this work, we would like to study how to place a sensor/access-point to maximize the coverage area within an arbitrary polygonal ROI. Our novel approach is based on finding the maximum-area clique over the visibility graph corresponding to the indoor geometry. According to many examples, our proposed optimal sensor/access-point scheme can lead to larger coverage efficiencies than the existing solution to the art gallery problem (AGP) and the conventional Delaunay triangulation method. Our new scheme is of great practical value as it can be applied for not only both convex and nonconvex simply-connected polygons but also both convex and nonconvex multiply-connected polygons with internal holes.

Novel Cascade Classifier Using Multiresolution Progressive Learning for Device-Free Indoor Localization

Indoor localization of human objects has many important applications nowadays. Here, we propose a new multiresolution device-free indoor localization scheme using wireless radio frequency (RF) fingerprints. In our proposed new device-free approach, all transceiver devices are fixed in an indoor environment, so human targets do not need to carry any transceiver device with them. First, the indoor geometry is divided into several zones, and then, the received signal strength indicators measured by the receiving antennas are input features to our designed innovative machine learning model to identify within which zone the target is. Our proposed machine learning model, i.e., a multiresolution random forest classifier, is composed of a cascade architecture, which integrates and distills learned results over various zoning resolutions. The proposed new multiresolution approach greatly outperforms the existing random forest classifier. The average Euclidean distance error resulting from our proposed new technique is 1.0376 m.

Novel Optimal Multisensor Placement for Indoor Rectilinear Line-of-Sight Coverage

Optimal multisensor placement/deployment for an arbitrary indoor geometry still remains very challenging nowadays. It is preferable to minimize the number of sensors which can still cover the entire indoor space with a balanced load. However, this multi-objective problem is quite complex in practice. In this work, we will study this critical multisensor placement problem mathematically and algorithmically and propose a systematic approach to tackle this problem. In this paper, we focus on the line-of-sight (LoS) coverage within a rectilinear indoor environment and the sensors’ locations are restricted on the perimeter. Our proposed new approach consists of two stages. First, a partitioning algorithm is designed to partition an arbitrary rectilinear geometry into a number (as few as possible) of feasible rectilinear subareas, each of which can be fully covered by a sensor located somewhere on its external perimeter. Second, the average squared Euclidean distance from a sensor to an arbitrary point within its coverage area is taken as an additional objective measure. By minimizing such average squared Euclidean distance, one can find the optimal sensor location. In our new approach, the first stage is related to solving the art gallery problem (AGP) based on discrete optimization while the second stage is based on continuous optimization. Therefore, our proposed new approach involves both continuous and discrete optimization schemes. By simulations, we also show that the number of sensors placed by our new algorithm is equal to or less than the theoretical upper-bound established in the existing AGP study for numerous rectilinear geometries. Moreover, we also show that by minimizing the average squared Euclidean distance, we maximize the average received signal strength indoors using the prevalent Feko wireless-channel emulator. Our proposed new systematic approach can also deal with practical scenarios involving internal obstacle(s) and sensing-range restriction.

Modeling of indoor airflow around thermal manikins by multiple-relaxation-time lattice Boltzmann method with LES approaches

As an effective CFD approach, Multiple-Relaxation-Time Lattice Boltzmann Method (MRT-LBM) has gradually gained its popularity in the past 20 years. In this article, a case that simulated the airflow patterns surrounding the human bodies were studied using MRT-LBM. The governing equation was solved by the MRT-LBM in the simulation. In order to calculate the boundary layer around the complicated geometry, wall-adapting local eddy viscosity (WALE) model was applied for the turbulence model. The D3Q19 and D3Q7 models were applied for the discretization of momentum and energy equation, respectively. In order to analyze the grid independence, three lattice densities were compared in this study. The simulation results were compared with the finite volume method (FVM) as well as the experimental data. Results showed that MRT-LBM with grid resolution of 0.01 m would obtain accurate results of airflow surrounding the human bodies. FVM required 5–6 times computation time than the MRT-LBM. Thereby, the MRT-LBM method can have advantages in the indoor airflow simulation field especially when the indoor geometry is complicated.

Experimental Characterization of Millimeter-Wave Indoor Propagation Channels at 28 GHz

The increasing requirement for the mobile data traffic accelerates the research of millimeter-wave (mm-wave) for future wireless systems. Accurate characterization of the mm-wave propagation channel is fundamental and essential for the system design and performance evaluation. In this paper, we conducted measurement campaigns in various indoor scenarios, including classroom, office, and hall scenarios, at the frequency bands of 27–29 GHz. The spatial channel characteristics were recorded by using a large-scale uniform circular array. A high-resolution parameter estimation algorithm was applied to estimate the mm-wave spherical propagation parameters, i.e., the azimuth angle, elevation angle, delay, source distance, and complex amplitude of multipath components. With the same measurement system, the channel parameters including decay factor, delay spread, angular spread, and line of sight power ratio are investigated thoroughly in individual indoor scenarios and compared in different indoor scenarios. Furthermore, the impact of the furniture richness level and indoor geometry on the propagation parameters are also investigated.

Automatically enhancing CityGML LOD2 models with a corresponding indoor geometry

In this paper we present two contributions: i the definition of a level of detail LOD2+, which extends the CityGML LOD2 specification with indoor building geometries of comparable complexity to the...

Automatically enhancing CityGML LOD2 models with a corresponding indoor geometry

The international standard CityGML defines five levels of detail (LODs) for 3D city models, but only the highest of these (LOD4) supports modelling the indoor geometry of a building, which must be acquired in correspondingly high detail and therefore at a high cost. Whereas simple 3D city models of the exterior of buildings (e.g. CityGML LOD2) can be generated largely automatically, and are thus now widely available and have a great variety of applications, similarly simple models containing their indoor geometries are rare.In this paper we present two contributions: (i) the definition of a level of detail LOD2+, which extends the CityGML LOD2 specification with indoor building geometries of comparable complexity to their exterior geometries in LOD2; and more importantly (ii) a method for automatically generating such indoor geometries based on existing CityGML LOD2 exterior geometries. We validate our method by generating LOD2+ models for a subset of the Rotterdam 3D data set and visually comparing these models to their real counterparts in building blueprints and imagery from Google Street View and Bing Maps. Furthermore, we use the LOD2+ models to compute the net internal area of each dwelling and validate our results by comparing these values to the ones registered in official government data sets.

GRAMMAR-SUPPORTED 3D INDOOR RECONSTRUCTION FROM POINT CLOUDS FOR “AS-BUILT” BIM

Abstract. The paper presents a grammar-based approach for the robust automatic reconstruction of 3D interiors from raw point clouds. The core of the approach is a 3D indoor grammar which is an extension of our previously published grammar concept for the modeling of 2D floor plans. The grammar allows for the modeling of buildings whose horizontal, continuous floors are traversed by hallways providing access to the rooms as it is the case for most office buildings or public buildings like schools, hospitals or hotels. The grammar is designed in such way that it can be embedded in an iterative automatic learning process providing a seamless transition from LOD3 to LOD4 building models. Starting from an initial low-level grammar, automatically derived from the window representations of an available LOD3 building model, hypotheses about indoor geometries can be generated. The hypothesized indoor geometries are checked against observation data - here 3D point clouds - collected in the interior of the building. The verified and accepted geometries form the basis for an automatic update of the initial grammar. By this, the knowledge content of the initial grammar is enriched, leading to a grammar with increased quality. This higher-level grammar can then be applied to predict realistic geometries to building parts where only sparse observation data are available. Thus, our approach allows for the robust generation of complete 3D indoor models whose quality can be improved continuously as soon as new observation data are fed into the grammar-based reconstruction process. The feasibility of our approach is demonstrated based on a real-world example.

Distance-Aware Join for Indoor Moving Objects

Indoor spaces accommodate large parts of people’s lives. Relevant techniques are thus needed to efficiently manage indoor moving objects, whose positions are detected by technologies, such as Assisted GPS, Wi-Fi, RFID, and Bluetooth. Among such techniques, the distance-aware join processing is of importance in practice for indoor spatial databases. Such join operators leverage a series of applications, such as indoor mobile service and facility monitoring. However, distance-aware joining over indoor moving objects is challenging because: (1) indoor spaces are characterized by many special entities and thus render distance calculation very complex; (2) the limitations of indoor positioning technologies create inherent uncertainties in indoor moving objects data. In this paper, we study two representative join predicates in indoor settings, semi-range join and semi-neighborhood join . To implement them, we define and categorize the indoor distances between indoor uncertain objects, and derive different distance bounds that can facilitate the join processing. We design a composite index scheme that integrates indoor geometries, indoor topologies, as well as indoor uncertain objects, and thus supports the join processing efficiently. The results of extensive experimental studies demonstrate that our proposals are efficient and scalable in evaluating distance-aware join over indoor moving objects.

Indoor Modelling Benchmark for 3D Geometry Extraction

Abstract. A combination of faster, cheaper and more accurate hardware, more sophisticated software, and greater industry acceptance have all laid the foundations for an increased desire for accurate 3D parametric models of buildings. Pointclouds are the data source of choice currently with static terrestrial laser scanning the predominant tool for large, dense volume measurement. The current importance of pointclouds as the primary source of real world representation is endorsed by CAD software vendor acquisitions of pointcloud engines in 2011. Both the capture and modelling of indoor environments require great effort in time by the operator (and therefore cost). Automation is seen as a way to aid this by reducing the workload of the user and some commercial packages have appeared that provide automation to some degree. In the data capture phase, advances in indoor mobile mapping systems are speeding up the process, albeit currently with a reduction in accuracy. As a result this paper presents freely accessible pointcloud datasets of two typical areas of a building each captured with two different capture methods and each with an accurate wholly manually created model. These datasets are provided as a benchmark for the research community to gauge the performance and improvements of various techniques for indoor geometry extraction. With this in mind, non-proprietary, interoperable formats are provided such as E57 for the scans and IFC for the reference model. The datasets can be found at: http://indoor-bench.github.io/indoor-bench.

Combined Grammar for the Modeling of Building Interiors

Abstract. As spatial grammars have proven successful and efficient to deliver LOD3 models, the next challenge is their extension to indoor applications, leading to LOD4 models. Therefore, a combined indoor grammar for the automatic generation of indoor models from erroneous and incomplete observation data is presented. In building interiors where inaccurate observation data is available, the grammar can be used to make the reconstruction process robust, and verify the reconstructed geometries. In unobserved building interiors, the grammar can generate hypotheses about possible indoor geometries matching the style of the rest of the building. The grammar combines concepts from L-systems and split grammars. It is designed in such way that it can be derived from observation data fully automatically. Thus, manual predefinitions of the grammar rules usually required to tune the grammar to a specific building style, become obsolete. The potential benefit of using our grammar as support for indoor modeling is evaluated based on an example where the grammar has been applied to automatically generate an indoor model from erroneous and incomplete traces gathered by foot-mounted MEMS/IMU positioning systems.

Spot diffusing angle diversity MC-CDMA optical wireless system

Multi-carrier code division multiple access (MC-CDMA) combines some of the desirable features of orthogonal frequency-division multiplexing (OFDM) and CDMA in that it offers multi-user capabilities at a reduced channel rate. The authors report the analysis of a MC-CDMA system over the bandlimited optical wireless (OW) channel. Furthermore, the authors evaluate the performance when a line strip multibeam system (LSMS) is used in conjunction with an angle diversity receiver in the OW MC-CDMA system. The results indicate that at a bit error rate of 10 -4 a 7 and 10 dB signal-to-noise ratio (SNR) improvements are obtained through the use of MC-CDMA in a 4-user system when compared to a single-user OW on off keying system and a 4-user OW CDMA system, respectively. This improvement is obtained by enabling multi-user communication. The performance degrades gradually with increase in the number of users. The use of LSMS and angle diversity offers a more uniform SNR over a given indoor geometry compared to a conventional diffuse OW system (CDS). Results are reported for an angle diversity LSMS MC-CDMA system and are compared to the CDS MC-CDMA system.

AUTOMATIC GEOMETRY-DRIVEN OFDM QUALITY-OF-SERVICE ANALYSIS FOR INDOOR ENVIRONMENTS

This paper introduces a novel geometry-based simulation technique for the arbitrary indoor OFDM quality-of-service (QoS) analysis. This automatic geometry-driven approach integrates electromagnetic radio propagation modeling, channel characterization, and OFDM interference analysis to quantify the indoor OFDM performance through the off-site computer–aided procedures. Essential OFDM quality-of-service measures, such as signal-to-interference ratios (SIR) and bit error rates (BER) can be generated by this new analysis tool. The resulting QoS measure contours can be used by service providers and OFDM communication system designers to quantify the system performance and determine the optimal accessing locations for any indoor geometry.

Derivation of indoor gamma dose rate from high resolution in situ gamma ray spectra.

The dose build up factor B related to the ratio of primary to scattered gamma radiation in indoor environment was calculated with Monte Carlo simulations using the MCNP code for different indoor geometries and gamma source distributions. The main conclusion is that the B factor does not depend strongly on parameters such as dimensions of the rooms, the thickness of walls, the density of the building materials, and the gamma source geometry. The calculated dose build up factors were used within the framework of the full absorption peak analysis method in order to deduce the dose rates in air from about 100 indoor in situ gamma spectrometry measurements performed at the town of Thessaloniki, Greece. A spectral "stripping method," which has been recently developed, was applied to the same 100 spectrometry measurements. The results of the dose rates obtained by the two methods are compared and discussed.