Search Results (31)

On 11 September 2001, attacks on the World Trade Center (WTC) killed nearly three thousand people and exposed hundreds of thousands of rescue and recovery workers, passersby, area workers, and residents to varying amounts of dust and smoke. Former New York City Mayor Rudy Giuliani ordered the emergency evacuation of Lower Manhattan below Canal Street, but not all residents evacuated. Previous studies showed that those who did not evacuate had a higher incidence of newly diagnosed asthma. Among the 71,424 who enrolled in the WTC Health Registry in 2003–2004, we evaluated the bivariate association of educational attainment, household income, and race or ethnicity with reported evacuation on or after 9/11/01. We used log binomial regression to assess the relative risks of not evacuating from their home following the 9/11 attacks, adjusting for age, gender, and marital status. Out of a total of 11,871 enrollee residents of Lower Manhattan, 7345 or 61.79% reported evacuating their home on or after 9/11. In a fully adjusted model, the estimated relative risk for not evacuating was elevated for those who identified as non-Hispanic Black, Asian/Pacific Islander, and Hispanic residents compared to non-Hispanic White residents. Residents with a high school diploma/GED had an elevated estimated risk compared to those with at least a bachelor’s degree. Those with lower household incomes had an elevated estimated risk compared to those with the highest income category. These significant inequities will need to be prevented in future disasters. Full article

In conventional point-line visual–inertial odometry systems in indoor environments, consideration of spatial position recovery and line feature classification can improve localization accuracy. In this paper, a monocular visual–inertial odometry based on structured and unstructured line features of vanishing points is proposed. First, the degeneracy phenomenon caused by a special geometric relationship between epipoles and line features is analyzed in the process of triangulation, and a degeneracy detection strategy is designed to determine the location of the epipoles. Then, considering that the vanishing point and the epipole coincide at infinity, the vanishing point feature is introduced to solve the degeneracy and direction vector optimization problem of line features. Finally, threshold constraints are used to categorize straight lines into structural and non-structural features under the Manhattan world assumption, and the vanishing point measurement model is added to the sliding window for joint optimization. Comparative tests on the EuRoC and TUM-VI public datasets validated the effectiveness of the proposed method. Full article

Point cloud data are used to create an as-built building information model (as-built BIM) that reflects the actual status of any building, whether being constructed or already completed. However, indoor clutter objects in the point cloud data, such as people, tools, and materials, should be effectively eliminated to create the as-built BIM. In this study, the authors proposed a novel method to automatically remove indoor clutter objects based on the Manhattan World assumption and object characteristics. Our method adopts a two-dimensional (2D) projection of a 3D point cloud approach and utilizes different properties of indoor clutter objects and structural elements in the point cloud. Voxel-grid downsampling, density-based spatial clustering (DBSCAN), the statistical outlier removal (SOR) filter, and the unsupervised radius-based nearest neighbor search algorithm were applied to our method. Based on the evaluation of our proposed method using six actual scan datasets, we found that our method achieved a higher mean accuracy (0.94), precision (0.97), recall (0.90), and F1 core (0.93) than the commercial point cloud processing software. Our method shows better results than commercial point cloud processing software in classifying and removing indoor clutter objects in complex indoor environments acquired from construction sites. As a result, assumptions about the different properties of indoor clutter objects and structural elements are being used to identify indoor clutter objects. Additionally, it is confirmed that the parameters used in the proposed method could be determined by the voxel size once it is decided during the downsampling process. Full article

This Special Issue gathers papers reporting research on various aspects of the use of low-cost photogrammetric and lidar sensors for indoor building reconstruction. It includes contributions presenting improvements in the alignment of mobile mapping systems with and without a prior 3D BIM model, the interpretation of both imagery and lidar data of indoor scenery and finally the reconstruction and enrichment of existing 3D point clouds and meshes with BIM information. Concretely, the publications showcase methods and experiments for the Reconstruction of Indoor Navigation Elements for Point Cloud of Buildings with Occlusions and Openings by Wall Segment Restoration from Indoor Context Labeling, Two-Step Alignment of Mixed Reality Devices to Existing Building Data, Pose Normalization of Indoor Mapping Datasets Partially Compliant with the Manhattan World Assumption, A Robust Rigid Registration Framework of 3D Indoor Scene Point Clouds Based on RGB-D Information, 3D Point Cloud Semantic Augmentation for Instance Segmentation of 360° Panoramas by Deep Learning Techniques and the Symmetry-Based Coarse Registration of Smartphone’s Colorful Point Clouds with CAD Drawings (RegARD) for Low-Cost Digital Twin Buildings. Full article

Simultaneous localization and mapping (SLAM) technology based on light detection and ranging (LiDAR) sensors has been widely used in various environmental sensing tasks indoors and outdoors. However, it still lacks effective constraints in structured environments such as corridors and parking lots, and its accuracy needs improvement. Based on this, a planar constraint-assisted LiDAR SLAM algorithm based on the Manhattan World (MW) assumption is proposed in this paper. The algorithm extracts planes from the environment point cloud submap, classifies the planes according to the ground and vertical planes, and calculates the main direction angles of the ground and vertical plane, respectively, to construct constraints. To enhance the stability and robustness of the system, a two-step main direction angle calculation and update strategy are designed, and a hysteresis update is used to avoid the introduction of errors by unoptimized planes. This paper uses a backpack laser scanning system to collect experimental data in various scenes. These data are used to compare our method with three open-source LiDAR SLAM algorithms, that are currently more widely used and perform better. Qualitative and quantitative experiments are conducted to verify the effectiveness of our method. The experimental results show that the absolute accuracy of the point clouds obtained by our method is improved by 77.46% on average compared with the other three algorithms in the environment, conforming to the MW assumption, which verifies the effectiveness of the algorithm. Full article

We propose a robust RGB-Depth (RGB-D) Visual Odometry (VO) system to improve the localization performance of indoor scenes by using geometric features, including point and line features. Previous VO/Simultaneous Localization and Mapping (SLAM) algorithms estimate the low-drift camera poses with the Manhattan World (MW)/Atlanta World (AW) assumption, which limits the applications of such systems. In this paper, we divide the indoor environments into two different scenes: MW and non-MW scenes. The Manhattan scenes are modeled as a Mixture of Manhattan Frames, in which each Manhattan Frame in itself defines a Manhattan World of a specific orientation. Moreover, we provide a method to detect Manhattan Frames (MFs) using the dominant directions extracted from the parallel lines. Our approach is designed with lower computational complexity than existing techniques using planes to detect Manhattan Frame (MF). For MW scenes, we separately estimate rotational and translational motion. A novel method is proposed to estimate the drift-free rotation using MF observations, unit direction vectors of lines, and surface normal vectors. Then, the translation part is recovered from point-line tracking. In non-MW scenes, the tracked and matched dominant directions are combined with the point and line features to estimate the full 6 degree of freedom (DoF) camera poses. Additionally, we exploit the rotation constraints generated from the multi-view dominant directions observations. The constraints are combined with the reprojection errors of points and lines to refine the camera pose through local map bundle adjustment. Evaluations on both synthesized and real-world datasets demonstrate that our approach outperforms state-of-the-art methods. On synthesized datasets, average localization accuracy is 1.5 cm, which is equivalent to state-of-the-art methods. On real-world datasets, the average localization accuracy is 1.7 cm, which outperforms the state-of-the-art methods by 43%. Our time consumption is reduced by 36%. Full article

An increasing number of applications require the accurate 3D layout reconstruction of indoor environments. Various devices including laser scanners and color and depth (RGB-D) cameras can be used for this purpose and provide abundant and highly precise data sources. However, due to indoor environment complexity, existing noise and occlusions caused by clutter in acquired data, current studies often require the idealization of the architecture space or add an implication hypothesis to input data as priors, which limits the use of these methods for general purposes. In this study, we propose a general 3D layout reconstruction method for indoor environments. The method combines voxel-based room segmentation and space partition to build optimum polygonal models. It releases idealization of the architectural space into a non-Manhattan world and can accommodate various types of input data sources, including both point clouds and meshes. A total of four point cloud datasets, four mesh datasets and two cross-floor datasets were used in experiments. The results exhibit more than 80% completeness and correctness as well as high accuracy. Full article

The World Trade Center Health Registry (WTCHR) was established in 2002 as a public health resource to monitor the health effects from the World Trade Center (WTC) disaster. We evaluated the representativeness of the WTC youth population (<18 years on 11 September 2001) by comparing the distributions of age, gender, race/ethnic groups, and income to 2000 census data for the matched geographic area, including distance from disaster. There were 2379 WTCHR enrolled children living in Lower Manhattan south of Canal Street on 11 September 2001, along with 752 enrolled students who attended school in Lower Manhattan but were not area residents. The WTCHR sub-group of children who were residents was similar to the geographically corresponding census population on age and sex. Black and Hispanic children are moderately overrepresented at 0.9% and 2.4% in the WTCHR compared to 0.8% and 1.7% in census population, respectively, while lower-income households are slightly under-represented, 28.8% in the WTCHR and 30.8% for the corresponding census information. Asian children appear underrepresented at 3.0% participation compared to 6.3% in the census. While the demographics of WTCHR youth are somewhat skewed, the gaps are within expected patterns of under-representation observed in other longitudinal cohorts and can be effectively addressed analytically or through targeted study design. Full article

This paper proposes a meaningful and effective extension of the celebrated K-means algorithm to detect communities in feature-rich networks, due to our assumption of non-summability mode. We least-squares approximate given matrices of inter-node links and feature values, leading to a straightforward extension of the conventional K-means clustering method as an alternating minimization strategy for the criterion. This works in a two-fold space, embracing both the network nodes and features. The metric used is a weighted sum of the squared Euclidean distances in the feature and network spaces. To tackle the so-called curse of dimensionality, we extend this to a version that uses the cosine distances between entities and centers. One more version of our method is based on the Manhattan distance metric. We conduct computational experiments to test our method and compare its performances with those by competing popular algorithms at synthetic and real-world datasets. The cosine-based version of the extended K-means typically wins at the high-dimension real-world datasets. In contrast, the Manhattan-based version wins at most synthetic datasets. Full article

Due to their great potential for a variety of applications, digital building models are well established in all phases of building projects. Older stock buildings however frequently lack digital representations, and creating these manually is a tedious and time-consuming endeavor. For this reason, the automated reconstruction of building models from indoor mapping data has arisen as an active field of research. In this context, many approaches rely on simplifying suppositions about the structure of buildings to be reconstructed such as, e.g., the well-known Manhattan World assumption. This however not only presupposes that a given building structure itself is compliant with this assumption, but also that the respective indoor mapping dataset is aligned with the coordinate axes. Indoor mapping systems, on the other hand, typically initialize the coordinate system arbitrarily by the sensor pose at the beginning of the mapping process. Thus, indoor mapping data need to be transformed from the local coordinate system, resulting from the mapping process, to a local coordinate system where the coordinate axes are aligned with the Manhattan World structure of the building. This necessary preprocessing step for many indoor reconstruction approaches is also frequently known as pose normalization. In this paper, we present a novel pose-normalization method for indoor mapping point clouds and triangle meshes that is robust against large portions of the indoor mapping geometries deviating from an ideal Manhattan World structure. In the case of building structures that contain multiple Manhattan World systems, the dominant Manhattan World structure supported by the largest fraction of geometries was determined and used for alignment. In a first step, a vertical alignment orienting a chosen axis to be orthogonal to horizontal floor and ceiling surfaces was conducted. Subsequently, a rotation around the resulting vertical axis was determined that aligned the dataset horizontally with the axes of the local coordinate system. The performance of the proposed method was evaluated quantitatively on several publicly available indoor mapping datasets of different complexity. The achieved results clearly revealed that our method is able to consistently produce correct poses for the considered datasets for different input rotations with high accuracy. The implementation of our method along with the code for reproducing the evaluation is made available to the public. Full article

Room segmentation is a basic task for the semantic enrichment of point clouds. Recent studies have mainly projected single-floor point clouds to binary images to realize two-dimensional room segmentation. However, these methods have difficulty solving semantic segmentation problems in complex 3D indoor environments, including cross-floor spaces and rooms inside rooms; this is the bottleneck of indoor 3D modeling for non-Manhattan worlds. To make full use of the abundant geometric and spatial structure information in 3D space, a novel 3D room segmentation method that realizes room segmentation directly in 3D space is proposed in this study. The method utilizes volumetric representation based on a VDB data structure and packs an indoor space with a set of compact spheres to form rooms as separated connected components. Experimental results on different types of indoor point cloud datasets demonstrate the efficiency of the proposed method. Full article

Laser scanning is a popular means of acquiring the indoor scene data of buildings for a wide range of applications concerning indoor environment. During data acquisition, unwanted data points beyond the indoor space of interest can also be recorded due to the presence of openings, such as windows and doors on walls. For better visualization and further modeling, it is beneficial to filter out those data, which is often achieved manually in practice. To automate this process, an efficient image-based filtering approach was explored in this research. In this approach, a binary mask image was created and updated through mathematical morphology operations, hole filling and connectively analysis. The final mask obtained was used to remove the data points located outside the indoor space of interest. The application of the approach to several point cloud datasets considered confirms its ability to effectively keep the data points in the indoor space of interest with an average precision of 99.50%. The application cases also demonstrate the computational efficiency (0.53 s, at most) of the approach proposed. Full article

Heritage buildings are often lost without being adequately documented. Significant research has gone into automated building modelling from point clouds, challenged by irregularities in building design and the presence of occlusion-causing clutter and non-Manhattan World features. Previous work has been largely focused on the extraction and representation of walls, floors, and ceilings from either interior or exterior single storey scans. Significantly less effort has been concentrated on the automated extraction of smaller features such as windows and doors from complete (interior and exterior) scans. In addition, the majority of the work done on automated building reconstruction pertains to the new-build and construction industries, rather than for heritage buildings. This work presents a novel multi-level storey separation technique as well as a novel door and window detection strategy within an end-to-end modelling software for the automated creation of 2D floor plans and 3D building models from complete terrestrial laser scans of heritage buildings. The methods are demonstrated on three heritage sites of varying size and complexity, achieving overall accuracies of 94.74% for multi-level storey separation and 92.75% for the building model creation. Additionally, the automated door and window detection methodology achieved absolute mean dimensional errors of 6.3 cm. Full article

Rising temperatures worldwide pose an existential threat to people, properties, and the environment. Urban areas are particularly vulnerable to temperature increases due to the heat island effect, which amplifies local heating. Throughout the world, several megacities experience summer temperatures that stress human survival. Generating very high-resolution temperature forecasts is a fundamental problem to mitigate the effects of urban warming. This paper uses the Analog Ensemble technique to downscale existing temperature forecast from a low resolution to a much higher resolution using private weather stations. A new downscaling approach, based on the reuse of the Analog Ensemble (AnEn) indices, resulted by the combination of days and Forecast Lead Time (FLT)s, is proposed. Specifically, temperature forecasts from the NAM-NMM Numerical Weather Prediction model at 12 km are downscaled using 83 Private Weather Stations data over Manhattan, New York City, New York. Forecasts for 84 h are generated, hourly for the first 36 h, and every three hours thereafter. The results are dense forecasts that capture the spatial variability of ambient conditions. The uncertainty associated with using non-vetted data is addressed. Full article

This article presents a cooperative optimization approach (COA) for distributing service points for mobility applications, which generalizes and refines a previously proposed method. COA is an iterative framework for optimizing service point locations, combining an optimization component with user interaction on a large scale and a machine learning component that learns user needs and provides the objective function for the optimization. The previously proposed COA was designed for mobility applications in which single service points are sufficient for satisfying individual user demand. This framework is generalized here for applications in which the satisfaction of demand relies on the existence of two or more suitably located service stations, such as in the case of bike/car sharing systems. A new matrix factorization model is used as surrogate objective function for the optimization, allowing us to learn and exploit similar preferences among users w.r.t. service point locations. Based on this surrogate objective function, a mixed integer linear program is solved to generate an optimized solution to the problem w.r.t. the currently known user information. User interaction, refinement of the matrix factorization, and optimization are iterated. An experimental evaluation analyzes the performance of COA with special consideration of the number of user interactions required to find near optimal solutions. The algorithm is tested on artificial instances, as well as instances derived from real-world taxi data from Manhattan. Results show that the approach can effectively solve instances with hundreds of potential service point locations and thousands of users, while keeping the user interactions reasonably low. A bound on the number of user interactions required to obtain full knowledge of user preferences is derived, and results show that with 50% of performed user interactions the solutions generated by COA feature optimality gaps of only 1.45% on average. Full article