scholarly journals LOW COST AND EFFICIENT 3D INDOOR MAPPING USING MULTIPLE CONSUMER RGB-D CAMERAS

2016 ◽  
Vol XLI-B1 ◽  
pp. 169-174
Author(s):  
C. Chen ◽  
B. S. Yang ◽  
S. Song
Keyword(s):  
Low Cost ◽  
Laser Scanner ◽  
Infrared Camera ◽  
Point Clouds ◽  
Field Of View ◽  
Large Field ◽  
Multiple Point ◽  
Seamless Integration ◽  
Ground Truth Data ◽  
Internal Parameters

Driven by the miniaturization, lightweight of positioning and remote sensing sensors as well as the urgent needs for fusing indoor and outdoor maps for next generation navigation, 3D indoor mapping from mobile scanning is a hot research and application topic. The point clouds with auxiliary data such as colour, infrared images derived from 3D indoor mobile mapping suite can be used in a variety of novel applications, including indoor scene visualization, automated floorplan generation, gaming, reverse engineering, navigation, simulation and etc. State-of-the-art 3D indoor mapping systems equipped with multiple laser scanners product accurate point clouds of building interiors containing billions of points. However, these laser scanner based systems are mostly expensive and not portable. Low cost consumer RGB-D Cameras provides an alternative way to solve the core challenge of indoor mapping that is capturing detailed underlying geometry of the building interiors. Nevertheless, RGB-D Cameras have a very limited field of view resulting in low efficiency in the data collecting stage and incomplete dataset that missing major building structures (e.g. ceilings, walls). Endeavour to collect a complete scene without data blanks using single RGB-D Camera is not technic sound because of the large amount of human labour and position parameters need to be solved. To find an efficient and low cost way to solve the 3D indoor mapping, in this paper, we present an indoor mapping suite prototype that is built upon a novel calibration method which calibrates internal parameters and external parameters of multiple RGB-D Cameras. Three Kinect sensors are mounted on a rig with different view direction to form a large field of view. The calibration procedure is three folds: 1, the internal parameters of the colour and infrared camera inside each Kinect are calibrated using a chess board pattern, respectively; 2, the external parameters between the colour and infrared camera inside each Kinect are calibrated using a chess board pattern; 3, the external parameters between every Kinect are firstly calculated using a pre-set calibration field and further refined by an iterative closet point algorithm. Experiments are carried out to validate the proposed method upon RGB-D datasets collected by the indoor mapping suite prototype. The effectiveness and accuracy of the proposed method is evaluated by comparing the point clouds derived from the prototype with ground truth data collected by commercial terrestrial laser scanner at ultra-high density. The overall analysis of the results shows that the proposed method achieves seamless integration of multiple point clouds form different RGB-D cameras collected at 30 frame per second.

scholarly journals LOW COST AND EFFICIENT 3D INDOOR MAPPING USING MULTIPLE CONSUMER RGB-D CAMERAS

2016 ◽  
Vol XLI-B1 ◽  
pp. 169-174 ◽  
Author(s):  
C. Chen ◽  
B. S. Yang ◽  
S. Song
Keyword(s):  
Low Cost ◽  
Laser Scanner ◽  
Infrared Camera ◽  
Point Clouds ◽  
Field Of View ◽  
Large Field ◽  
Multiple Point ◽  
Seamless Integration ◽  
Ground Truth Data ◽  
Internal Parameters

Driven by the miniaturization, lightweight of positioning and remote sensing sensors as well as the urgent needs for fusing indoor and outdoor maps for next generation navigation, 3D indoor mapping from mobile scanning is a hot research and application topic. The point clouds with auxiliary data such as colour, infrared images derived from 3D indoor mobile mapping suite can be used in a variety of novel applications, including indoor scene visualization, automated floorplan generation, gaming, reverse engineering, navigation, simulation and etc. State-of-the-art 3D indoor mapping systems equipped with multiple laser scanners product accurate point clouds of building interiors containing billions of points. However, these laser scanner based systems are mostly expensive and not portable. Low cost consumer RGB-D Cameras provides an alternative way to solve the core challenge of indoor mapping that is capturing detailed underlying geometry of the building interiors. Nevertheless, RGB-D Cameras have a very limited field of view resulting in low efficiency in the data collecting stage and incomplete dataset that missing major building structures (e.g. ceilings, walls). Endeavour to collect a complete scene without data blanks using single RGB-D Camera is not technic sound because of the large amount of human labour and position parameters need to be solved. To find an efficient and low cost way to solve the 3D indoor mapping, in this paper, we present an indoor mapping suite prototype that is built upon a novel calibration method which calibrates internal parameters and external parameters of multiple RGB-D Cameras. Three Kinect sensors are mounted on a rig with different view direction to form a large field of view. The calibration procedure is three folds: 1, the internal parameters of the colour and infrared camera inside each Kinect are calibrated using a chess board pattern, respectively; 2, the external parameters between the colour and infrared camera inside each Kinect are calibrated using a chess board pattern; 3, the external parameters between every Kinect are firstly calculated using a pre-set calibration field and further refined by an iterative closet point algorithm. Experiments are carried out to validate the proposed method upon RGB-D datasets collected by the indoor mapping suite prototype. The effectiveness and accuracy of the proposed method is evaluated by comparing the point clouds derived from the prototype with ground truth data collected by commercial terrestrial laser scanner at ultra-high density. The overall analysis of the results shows that the proposed method achieves seamless integration of multiple point clouds form different RGB-D cameras collected at 30 frame per second.

scholarly journals CAN WE USE LOW-COST 360 DEGREE CAMERAS TO CREATE ACCURATE 3D MODELS?

2018 ◽  
Vol XLII-2 ◽  
pp. 69-75 ◽  
Author(s):  
L. Barazzetti ◽  
M. Previtali ◽  
F. Roncoroni
Keyword(s):  
Laser Scanning ◽  
Low Cost ◽  
Point Clouds ◽  
3D Models ◽  
Bundle Adjustment ◽  
Field Of View ◽  
Large Field ◽  
Single Shot ◽  
New Paradigm ◽  
Image Orientation

360 degree cameras capture the whole scene around a photographer in a single shot. Cheap 360 cameras are a new paradigm in photogrammetry. The camera can be pointed to any direction, and the large field of view reduces the number of photographs. This paper aims to show that accurate metric reconstructions can be achieved with affordable sensors (less than 300 euro). The camera used in this work is the Xiaomi Mijia Mi Sphere 360, which has a cost of about 300 USD (January 2018). Experiments demonstrate that millimeter-level accuracy can be obtained during the image orientation and surface reconstruction steps, in which the solution from 360° images was compared to check points measured with a total station and laser scanning point clouds. The paper will summarize some practical rules for image acquisition as well as the importance of ground control points to remove possible deformations of the network during bundle adjustment, especially for long sequences with unfavorable geometry. The generation of orthophotos from images having a 360° field of view (that captures the entire scene around the camera) is discussed. Finally, the paper illustrates some case studies where the use of a 360° camera could be a better choice than a project based on central perspective cameras. Basically, 360° cameras become very useful in the survey of long and narrow spaces, as well as interior areas like small rooms.

scholarly journals To 3D or Not 3D: Choosing a Photogrammetry Workflow for Cultural Heritage Groups

Heritage ◽  
2019 ◽  
Vol 2 (3) ◽  
pp. 1835-1851 ◽  
Author(s):  
Hafizur Rahaman ◽  
Erik Champion
Keyword(s):  
Low Cost ◽  
Laser Scanner ◽  
Ground Truth ◽  
Point Clouds ◽  
3D Models ◽  
3D Modelling ◽  
Quality Data ◽  
Average Deviation ◽  
Ground Truth Data ◽  
Software Application

The 3D reconstruction of real-world heritage objects using either a laser scanner or 3D modelling software is typically expensive and requires a high level of expertise. Image-based 3D modelling software, on the other hand, offers a cheaper alternative, which can handle this task with relative ease. There also exists free and open source (FOSS) software, with the potential to deliver quality data for heritage documentation purposes. However, contemporary academic discourse seldom presents survey-based feature lists or a critical inspection of potential production pipelines, nor typically provides direction and guidance for non-experts who are interested in learning, developing and sharing 3D content on a restricted budget. To address the above issues, a set of FOSS were studied based on their offered features, workflow, 3D processing time and accuracy. Two datasets have been used to compare and evaluate the FOSS applications based on the point clouds they produced. The average deviation to ground truth data produced by a commercial software application (Metashape, formerly called PhotoScan) was used and measured with CloudCompare software. 3D reconstructions generated from FOSS produce promising results, with significant accuracy, and are easy to use. We believe this investigation will help non-expert users to understand the photogrammetry and select the most suitable software for producing image-based 3D models at low cost for visualisation and presentation purposes.

scholarly journals Estimating tree stem diameters and volume from smartphone photogrammetric point clouds

2019 ◽  
Vol 93 (3) ◽  
pp. 411-429 ◽  
Author(s):  
Maria Immacolata Marzulli ◽  
Pasi Raumonen ◽  
Roberto Greco ◽  
Manuela Persia ◽  
Patrizia Tartarino
Keyword(s):  
Low Cost ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Point Clouds ◽  
Stem Volume ◽  
Forest Plot ◽  
3D Point Clouds ◽  
Dense Point ◽  
Minimum Number ◽  
Scale Point

Abstract Methods for the three-dimensional (3D) reconstruction of forest trees have been suggested for data from active and passive sensors. Laser scanner technologies have become popular in the last few years, despite their high costs. Since the improvements in photogrammetric algorithms (e.g. structure from motion—SfM), photographs have become a new low-cost source of 3D point clouds. In this study, we use images captured by a smartphone camera to calculate dense point clouds of a forest plot using SfM. Eighteen point clouds were produced by changing the densification parameters (Image scale, Point density, Minimum number of matches) in order to investigate their influence on the quality of the point clouds produced. In order to estimate diameter at breast height (d.b.h.) and stem volumes, we developed an automatic method that extracts the stems from the point cloud and then models them with cylinders. The results show that Image scale is the most influential parameter in terms of identifying and extracting trees from the point clouds. The best performance with cylinder modelling from point clouds compared to field data had an RMSE of 1.9 cm and 0.094 m3, for d.b.h. and volume, respectively. Thus, for forest management and planning purposes, it is possible to use our photogrammetric and modelling methods to measure d.b.h., stem volume and possibly other forest inventory metrics, rapidly and without felling trees. The proposed methodology significantly reduces working time in the field, using ‘non-professional’ instruments and automating estimates of dendrometric parameters.

scholarly journals ASSESSING THE ACCURACY AND PRECISION OF IMPERFECT POINT CLOUDS FOR 3D INDOOR MAPPING AND MODELING

2018 ◽  
Vol IV-4/W6 ◽  
pp. 3-10
Author(s):  
J. Chen ◽  
O. E. Mora ◽  
K. C. Clarke
Keyword(s):  
Low Cost ◽  
Random Noise ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Point Clouds ◽  
Accuracy And Precision ◽  
Wide Range ◽  
Sharp Edges ◽  
The Cost ◽  
Indoor Spaces

<p><strong>Abstract.</strong> In recent years, growing public interest in three-dimensional technology has led to the emergence of affordable platforms that can capture 3D scenes for use in a wide range of consumer applications. These platforms are often widely available, inexpensive, and can potentially find dual use in taking measurements of indoor spaces for creating indoor maps. Their affordability, however, usually comes at the cost of reduced accuracy and precision, which becomes more apparent when these instruments are pushed to their limits to scan an entire room. The point cloud measurements they produce often exhibit systematic drift and random noise that can make performing comparisons with accurate data difficult, akin to trying to compare a fuzzy trapezoid to a perfect square with sharp edges. This paper outlines a process for assessing the accuracy and precision of these imperfect point clouds in the context of indoor mapping by integrating techniques such as the extended Gaussian image, iterative closest point registration, and histogram thresholding. A case study is provided at the end to demonstrate use of this process for evaluating the performance of the Scanse Sweep 3D, an ultra-low cost panoramic laser scanner.</p>

scholarly journals COMBINATION OF TLS POINT CLOUDS AND 3D DATA FROM KINECT V2 SENSOR TO COMPLETE INDOOR MODELS

2016 ◽  
Vol XLI-B5 ◽  
pp. 659-666 ◽  
Author(s):  
E. Lachat ◽  
T. Landes ◽  
P. Grussenmeyer
Keyword(s):  
Point Cloud ◽  
Low Cost ◽  
Measurement Techniques ◽  
Laser Scanner ◽  
Point Clouds ◽  
Final Model ◽  
Kinect V2 ◽  
Multiple Sensors ◽  
3D Data ◽  
Final Data

The combination of data coming from multiple sensors is more and more applied for remote sensing issues (multi-sensor imagery) but also in cultural heritage or robotics, since it often results in increased robustness and accuracy of the final data. In this paper, the reconstruction of building elements such as window frames or door jambs scanned thanks to a low cost 3D sensor (Kinect v2) is presented. Their combination within a global point cloud of an indoor scene acquired with a terrestrial laser scanner (TLS) is considered. If the added elements acquired with the Kinect sensor enable to reach a better level of detail of the final model, an adapted acquisition protocol may also provide several benefits as for example time gain. The paper aims at analyzing whether the two measurement techniques can be complementary in this context. The limitations encountered during the acquisition and reconstruction steps are also investigated.

scholarly journals Assessing Low-Cost Terrestrial Laser Scanners for Deriving Forest Structure Parameters

2021 ◽  
Author(s):  
Atticus E. L. Stovall ◽  
Jeff W Atkins
Keyword(s):  
Structural Parameters ◽  
Low Cost ◽  
Laser Scanner ◽  
Point Clouds ◽  
Model Reconstruction ◽  
Tree Model ◽  
Factors Affecting ◽  
Laser Scanners ◽  
Tree Models ◽  
Profile Reconstruction

The increasingly affordable price point of terrestrial laser scanners has led to a democratization of instrument availability, but the most common low-cost instruments have yet to be compared in terms of the consistency to measure forest structural attributes. Here, we compared two low-cost terrestrial laser scanners (TLS): the Leica BLK360 and the Faro Focus 120 3D. We evaluate the instruments in terms of point cloud quality, forest inventory estimates, tree-model reconstruction, and foliage profile reconstruction. Our direct comparison of the point clouds showed reduced noise in filtered Leica data. Tree diameter and height were consistent across instruments (4.4% and 1.4% error, respectively). Volumetric tree models were less consistent across instruments, with ~29% bias, depending on model reconstruction quality. In the process of comparing foliage profiles, we conducted a sensitivity analysis of factors affecting foliage profile estimates, showing a minimal effect from instrument maximum range (for forests less than ~50 m in height) and surprisingly little impact from degraded scan resolution. Filtered unstructured TLS point clouds must be artificially re-gridded to provide accurate foliage profiles. The factors evaluated in this comparison point towards necessary considerations for future low-cost laser scanner development and application in detecting forest structural parameters.

A low-cost, large field-of-view scanning ion conductance microscope for studying nanoparticle–cell membrane interactions

Nanoscale ◽  
2017 ◽  
Vol 9 (37) ◽  
pp. 14172-14183 ◽  
Author(s):  
Astrid Gesper ◽  
Philipp Hagemann ◽  
Patrick Happel
Keyword(s):  
High Resolution ◽  
Cell Membrane ◽  
Low Cost ◽  
Field Of View ◽  
Large Field ◽  
Membrane Interactions ◽  
Ion Conductance

We present an improved Scanning Ion Conductance Microscope that allows high-resolution studies of the interaction of nanoparticles and the cell membrane.

scholarly journals 3D SUPER-RESOLUTION APPROACH FOR SPARSE LASER SCANNER DATA

2015 ◽  
Vol II-3/W5 ◽  
pp. 151-157 ◽  
Author(s):  
S. Hosseinyalamdary ◽  
A. Yilmaz
Keyword(s):  
Anisotropic Diffusion ◽  
Low Cost ◽  
Laser Scanner ◽  
Super Resolution ◽  
Point Clouds ◽  
Superior Performance ◽  
Laser Scanners ◽  
Anisotropic Diffusion Equation ◽  
Tracking Object ◽  
High Level

Laser scanner point cloud has been emerging in Photogrammetry and computer vision to achieve high level tasks such as object tracking, object recognition and scene understanding. However, low cost laser scanners are noisy, sparse and prone to systematic errors. This paper proposes a novel 3D super resolution approach to reconstruct surface of the objects in the scene. This method works on sparse, unorganized point clouds and has superior performance over other surface recovery approaches. Since the proposed approach uses anisotropic diffusion equation, it does not deteriorate the object boundaries and it preserves topology of the object.

Sign in / Sign up

Export Citation Format

Share Document