scholarly journals BIM-Based Registration and Localization of 3D Point Clouds of Indoor Scenes Using Geometric Features for Augmented Reality

2020 ◽  
Vol 12 (14) ◽  
pp. 2302 ◽  
Author(s):  
Bilawal Mahmood ◽  
SangUk Han ◽  
Dong-Eun Lee
Keyword(s):  
Augmented Reality ◽  
Cross Section ◽  
Point Clouds ◽  
Search Space ◽  
Facility Management ◽  
Geometric Feature ◽  
Indoor Environments ◽  
Model Matching ◽  
Registration Accuracy ◽  
3D Point Clouds

Augmented reality can improve construction and facility management by visualizing an as-planned model on its corresponding surface for fast, easy, and correct information retrieval. This requires the localization registration of an as-built model in an as-planned model. However, the localization and registration of indoor environments fail, owing to self-similarity in an indoor environment, relatively large as-planned models, and the presence of additional unplanned objects. Therefore, this paper proposes a computer vision-based method to (1) homogenize indoor as-planned and as-built models, (2) reduce the search space of model matching, and (3) localize the structure (e.g., room) for registration of the scanned area in its as-planned model. This method extracts a representative horizontal cross section from the as-built and as-planned point clouds to make these models similar, restricts unnecessary transformation to reduce the search space, and corresponds the line features for the estimation of the registration transformation matrix. The performance of this method, in terms of registration accuracy, is evaluated on as-built point clouds of rooms and a hallway on a building floor. A rotational error of 0.005 rad and a translational error of 0.088 m are observed in the experiments. Hence, the geometric feature described on a representative cross section with transformation restrictions can be a computationally cost-effective solution for indoor localization and registration.

scholarly journals Fast and Robust Flight Altitude Estimation of Multirotor UAVs in Dynamic Unstructured Environments Using 3D Point Cloud Sensors

Aerospace ◽  
2018 ◽  
Vol 5 (3) ◽  
pp. 94 ◽  
Author(s):  
Hriday Bavle ◽  
Jose Sanchez-Lopez ◽  
Paloma Puente ◽  
Alejandro Rodriguez-Ramos ◽  
Carlos Sampedro ◽  
...  
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Estimation Algorithm ◽  
Indoor Environments ◽  
3D Point Cloud ◽  
Flight Altitude ◽  
Cloud Data ◽  
3D Point Clouds ◽  
Laser Altimeters ◽  
Altitude Estimation

This paper presents a fast and robust approach for estimating the flight altitude of multirotor Unmanned Aerial Vehicles (UAVs) using 3D point cloud sensors in cluttered, unstructured, and dynamic indoor environments. The objective is to present a flight altitude estimation algorithm, replacing the conventional sensors such as laser altimeters, barometers, or accelerometers, which have several limitations when used individually. Our proposed algorithm includes two stages: in the first stage, a fast clustering of the measured 3D point cloud data is performed, along with the segmentation of the clustered data into horizontal planes. In the second stage, these segmented horizontal planes are mapped based on the vertical distance with respect to the point cloud sensor frame of reference, in order to provide a robust flight altitude estimation even in presence of several static as well as dynamic ground obstacles. We validate our approach using the IROS 2011 Kinect dataset available in the literature, estimating the altitude of the RGB-D camera using the provided 3D point clouds. We further validate our approach using a point cloud sensor on board a UAV, by means of several autonomous real flights, closing its altitude control loop using the flight altitude estimated by our proposed method, in presence of several different static as well as dynamic ground obstacles. In addition, the implementation of our approach has been integrated in our open-source software framework for aerial robotics called Aerostack.

scholarly journals Localization of Windows and Doors in 3d Point Clouds of Facades

2014 ◽  
Vol II-3 ◽  
pp. 87-94 ◽  
Author(s):  
W. Nguatem ◽  
M. Drauschke ◽  
H. Mayer
Keyword(s):  
Bayes Factor ◽  
Likelihood Function ◽  
Parallel Implementation ◽  
Point Clouds ◽  
Search Space ◽  
Shape Space ◽  
Data Sets ◽  
3D Point Clouds ◽  
Spline Curves ◽  
Search Routine

In this paper, we present a fully automatic approach to localize the outlines of facade objects (windows and doors) in 3D point clouds of facades. We introduce an approach to search for the main facade wall and locate the facade objects within a probabilistic framework. Our search routine is based on Monte Carlo Simulation (MC-Simulation). Templates containing control points of curves are used to approximate the possible shapes of windows and doors. These are interpolated using parametric B-spline curves. These templates are scored in a sliding window style over the entire facade using a likelihood function in a probabilistic matching procedure. This produces many competing results for which a two layered model selection based on Bayes factor is applied. A major thrust in our work is the introduction of a 2D shape-space of similar shapes under affine transform in this architectural scene. This transforms the initial parametric B-splines curves representing the outlines of objects to curves of affine similarity in a strongly reduced dimensionality thus facilitating the generation of competing hypotheses within the search space. A further computational speedup is achieved through the clustering of the search space to disjoint regions, thus enabling a parallel implementation. We obtain state-of-the results on self-acquired data sets. The robustness of our algorithm is evaluated on 3D point clouds from image matching and LiDAR data of diverse quality.

scholarly journals APPLICATIONS OF PANORAMIC IMAGES: FROM 720° PANORAMA TO INTERIOR 3D MODELS OF AUGMENTED REALITY

2015 ◽  
Vol XL-4/W5 ◽  
pp. 189-192 ◽  
Author(s):  
I.-C. Lee ◽  
F. Tsai
Keyword(s):  
Augmented Reality ◽  
Focal Length ◽  
Three Dimensional ◽  
Point Clouds ◽  
3D Models ◽  
Exterior Orientation ◽  
3D Point Clouds ◽  
Panoramic Images ◽  
Main Station ◽  
Orientation Parameters

A series of panoramic images are usually used to generate a 720° panorama image. Although panoramic images are typically used for establishing tour guiding systems, in this research, we demonstrate the potential of using panoramic images acquired from multiple sites to create not only 720° panorama, but also three-dimensional (3D) point clouds and 3D indoor models. Since 3D modeling is one of the goals of this research, the location of the panoramic sites needed to be carefully planned in order to maintain a robust result for close-range photogrammetry. After the images are acquired, panoramic images are processed into 720° panoramas, and these panoramas which can be used directly as panorama guiding systems or other applications. <br><br> In addition to these straightforward applications, interior orientation parameters can also be estimated while generating 720° panorama. These parameters are focal length, principle point, and lens radial distortion. The panoramic images can then be processed with closerange photogrammetry procedures to extract the exterior orientation parameters and generate 3D point clouds. In this research, VisaulSFM, a structure from motion software is used to estimate the exterior orientation, and CMVS toolkit is used to generate 3D point clouds. Next, the 3D point clouds are used as references to create building interior models. In this research, Trimble Sketchup was used to build the model, and the 3D point cloud was added to the determining of locations of building objects using plane finding procedure. In the texturing process, the panorama images are used as the data source for creating model textures. This 3D indoor model was used as an Augmented Reality model replacing a guide map or a floor plan commonly used in an on-line touring guide system. <br><br> The 3D indoor model generating procedure has been utilized in two research projects: a cultural heritage site at Kinmen, and Taipei Main Station pedestrian zone guidance and navigation system. The results presented in this paper demonstrate the potential of using panoramic images to generate 3D point clouds and 3D models. However, it is currently a manual and labor-intensive process. A research is being carried out to Increase the degree of automation of these procedures.

scholarly journals DGANet: A Dilated Graph Attention-Based Network for Local Feature Extraction on 3D Point Clouds

2021 ◽  
Vol 13 (17) ◽  
pp. 3484
Author(s):  
Jie Wan ◽  
Zhong Xie ◽  
Yongyang Xu ◽  
Ziyin Zeng ◽  
Ding Yuan ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Long Range ◽  
Point Clouds ◽  
Feature Representation ◽  
Learning Ability ◽  
Geometric Feature ◽  
Cloud Data ◽  
Fine Grained ◽  
3D Point Clouds ◽  
Benchmark Datasets

Feature extraction on point clouds is an essential task when analyzing and processing point clouds of 3D scenes. However, there still remains a challenge to adequately exploit local fine-grained features on point cloud data due to its irregular and unordered structure in a 3D space. To alleviate this problem, a Dilated Graph Attention-based Network (DGANet) with a certain feature for learning ability is proposed. Specifically, we first build a local dilated graph-like region for each input point to establish the long-range spatial correlation towards its corresponding neighbors, which allows the proposed network to access a wider range of geometric information of local points with their long-range dependencies. Moreover, by integrating the dilated graph attention module (DGAM) implemented by a novel offset–attention mechanism, the proposed network promises to highlight the differing ability of each edge of the constructed local graph to uniquely learn the discrepancy feature of geometric attributes between the connected point pairs. Finally, all the learned edge attention features are further aggregated, allowing the most significant geometric feature representation of local regions by the graph–attention pooling to fully extract local detailed features for each point. The validation experiments using two challenging benchmark datasets demonstrate the effectiveness and powerful generation ability of our proposed DGANet in both 3D object classification and segmentation tasks.

scholarly journals INDOOR PHOTOGRAMMETRY AIDED WITH UWB NAVIGATION

2018 ◽  
Vol XLII-2 ◽  
pp. 683-690 ◽  
Author(s):  
A. Masiero ◽  
F. Fissore ◽  
A. Guarnieri ◽  
A. Vettore
Keyword(s):  
Point Cloud ◽  
Inertial Sensors ◽  
Point Clouds ◽  
External Information ◽  
Indoor Environments ◽  
Metric Reconstruction ◽  
North East ◽  
3D Point Clouds ◽  
Significant Interest ◽  
The Subject

The subject of photogrammetric surveying with mobile devices, in particular smartphones, is becoming of significant interest in the research community. Nowadays, the process of providing 3D point clouds with photogrammetric procedures is well known. However, external information is still typically needed in order to move from the point cloud obtained from images to a 3D metric reconstruction. This paper investigates the integration of information provided by an UWB positioning system with visual based reconstruction to produce a metric reconstruction. Furthermore, the orientation (with respect to North-East directions) of the obtained model is assessed thanks to the use of inertial sensors included in the considered UWB devices. Results of this integration are shown on two case studies in indoor environments.

Pantomime

2021 ◽  
Vol 5 (1) ◽  
pp. 1-27
Author(s):  
Sameera Palipana ◽  
Dariush Salami ◽  
Luis A. Leiva ◽  
Stephan Sigg
Keyword(s):  
Point Cloud ◽  
Continuous Wave ◽  
Spatial Information ◽  
Point Clouds ◽  
Recognition System ◽  
Indoor Environments ◽  
Unique Region ◽  
Temporal Properties ◽  
3D Point Clouds ◽  
Wave Radar

We introduce Pantomime, a novel mid-air gesture recognition system exploiting spatio-temporal properties of millimeter-wave radio frequency (RF) signals. Pantomime is positioned in a unique region of the RF landscape: mid-resolution mid-range high-frequency sensing, which makes it ideal for motion gesture interaction. We configure a commercial frequency-modulated continuous-wave radar device to promote spatial information over the temporal resolution by means of sparse 3D point clouds and contribute a deep learning architecture that directly consumes the point cloud, enabling real-time performance with low computational demands. Pantomime achieves 95% accuracy and 99% AUC in a challenging set of 21 gestures articulated by 41 participants in two indoor environments, outperforming four state-of-the-art 3D point cloud recognizers. We further analyze the effect of the environment in 5 different indoor environments, the effect of articulation speed, angle, and the distance of the person up to 5m. We have publicly made available the collected mmWave gesture dataset consisting of nearly 22,000 gesture instances along with our radar sensor configuration, trained models, and source code for reproducibility. We conclude that pantomime is resilient to various input conditions and that it may enable novel applications in industrial, vehicular, and smart home scenarios.

scholarly journals INFLUENCE OF CO-ALIGNMENT PROCEDURES ON THE CO-REGISTRATION ACCURACY OF MULTI-EPOCH SFM POINTS CLOUDS

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 231-238
Author(s):  
M. Saponaro ◽  
A. Capolupo ◽  
G. Caporusso ◽  
E. Tarantino
Keyword(s):  
High Performance ◽  
Research Work ◽  
Point Clouds ◽  
Scalar Fields ◽  
Registration Accuracy ◽  
3D Point Clouds ◽  
Digital Elevation ◽  
Remotely Piloted Aircraft ◽  
Dense Point ◽  
Traditional Approaches

Abstract. The well-established spread of Remotely Piloted Aircraft Systems (RPAS) as high-performance devices in the acquisition of huge datasets has found a fertile field in the geomorphological change detection in coastal areas. The ability to retrieve image datasets with multi-epoch frequency makes them effectively incisive for planning ongoing monitoring. Considering the wide accessibility to multiple Structure-from-Motion (SfM)-3D point clouds, it follows the need for their proper management to identify a profitable co-registration approach valid for a proper comparison among them. In most cases the co-registration is inherited from the same georeferencing; in other cases, it can be done manually. Unfortunately, these methodologies are time consuming and often do not properly consider geometric errors on the models. The purpose of this research work was therefore to analyse an alternative method such as the co-alignment of sparse point clouds. Given the independently or co-aligned processed multi-epoch datasets, mean errors (ME) and root-mean-square error (RMSE) on Check Points (CPs) were evaluated by adopting different georeferencing strategies. Lastly, by first generating dense point clouds and from these the Digital Elevation Models (DEMs), scalar fields regarding DEM of Differences (DoD) were computed and allowed to localize any uncertainties δz among the estimated elevations. A cloud-to-cloud comparison was obtained using the M3C2 algorithm to extrapolate systematic georeferencing errors and the local deviation between models, an evidence of how the method can affect the detectable changes. The co-alignment methodology showed encouraging results proving to be a valid alternative to more traditional approaches.

scholarly journals 3D BUILDING RECONSTRUCTION BY MULTIVIEW IMAGES AND THE INTEGRATED APPLICATION WITH AUGMENTED REALITY

2016 ◽  
Vol XLI-B1 ◽  
pp. 1235-1241 ◽  
Author(s):  
Jin-Tsong Hwang ◽  
Ting-Chen Chu
Keyword(s):  
Urban Planning ◽  
Augmented Reality ◽  
Point Clouds ◽  
Shielding Effect ◽  
Modern Approach ◽  
Building Model ◽  
Application Service ◽  
3D Point Clouds ◽  
Multiview Images ◽  
Design Skills

This study presents an approach wherein photographs with a high degree of overlap are clicked using a digital camera and used to generate three-dimensional (3D) point clouds via feature point extraction and matching. To reconstruct a building model, an unmanned aerial vehicle (UAV) is used to click photographs from vertical shooting angles above the building. Multiview images are taken from the ground to eliminate the shielding effect on UAV images caused by trees. Point clouds from the UAV and multiview images are generated via Pix4Dmapper. By merging two sets of point clouds via tie points, the complete building model is reconstructed. The 3D models are reconstructed using AutoCAD 2016 to generate vectors from the point clouds; SketchUp Make 2016 is used to rebuild a complete building model with textures. To apply 3D building models in urban planning and design, a modern approach is to rebuild the digital models; however, replacing the landscape design and building distribution in real time is difficult as the frequency of building replacement increases. One potential solution to these problems is augmented reality (AR). Using Unity3D and Vuforia to design and implement the smartphone application service, a markerless AR of the building model can be built. This study is aimed at providing technical and design skills related to urban planning, urban designing, and building information retrieval using AR.

Generation of Dense 3D Point Clouds Using a Small Quadcopter

GEOMATICA ◽  
2014 ◽  
Vol 68 (4) ◽  
pp. 319-330
Author(s):  
Julien Li-Chee-Ming ◽  
Costas Armenakis
Keyword(s):  
Point Clouds ◽  
Bundle Adjustment ◽  
Disruptive Technology ◽  
Surface Model ◽  
Indoor Environments ◽  
Building Model ◽  
3D Point Clouds ◽  
Video Images ◽  
Laser Scanners ◽  
3D Building Model

The disruptive technology of small and light-weight Unmanned Vehicle Systems (UVS) is changing geo matics applications and creating new and innovative opportunities for measuring and modeling outdoor and indoor environments. UVS’ compliment, and in many cases replace, total stations and laser scanners, and operate as flexible mobile survey systems. A small aerial quadcopter has been used to capture oblique highly overlapped video images. The video images captured from the onboard camera were reconstructed based on an incremental structure from motion bundle adjustment approach. Dense multi-image matching was used to generate a 3D point cloud representing the surface of the mapped area from the oblique video images, while the position and orientation of the image frames were also estimated in the process. The pho togrammetrically-generated dense digital surface model was co-registered and compared to an existing 3D building model. The paper presents the methodology and the obtained results and accuracies.

Geometric and topological feature extraction of linear segments from 2D cross-section data of 3D point clouds

2015 ◽  
Author(s):  
Rajesh Ramamurthy ◽  
Kevin Harding ◽  
Xiaoming Du ◽  
Vincent Lucas ◽  
Yi Liao ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Cross Section ◽  
Point Clouds ◽  
Topological Feature ◽  
Cross Section Data ◽  
Section Data ◽  
3D Point Clouds
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