A Point Cloud-Based Method for Object Alignment Verification for Augmented Reality Applications

2015 ◽  
Author(s):  
Rafael Radkowski
Keyword(s):  
Maximum Likelihood ◽  
Augmented Reality ◽  
Maximum Likelihood Estimate ◽  
Point Cloud ◽  
Error Model ◽  
High Fidelity ◽  
Tracking Data ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Cloud Tracking

The paper introduces a method for an augmented reality (AR) assembly assistance application that allows one to quantify the alignment of two parts. Point cloud-based tracking is one method to recognize and to track physical parts. However, the correct fitting of two parts cannot be determined with high fidelity from point cloud tracking data due to occlusion and other challenges. A Maximum Likelihood Estimate (MLE) of an error model is suggested to quantify the probability that two parts are correctly aligned. An initial solution was investigated. The results of an offline-simulation with point cloud data are promising and indicate the efficacy of the suggested method.

scholarly journals Augmented reality system using lidar point cloud data for displaying dimensional information of objects on mobile phones

2014 ◽  
Vol II-5 ◽  
pp. 153-159 ◽  
Author(s):  
S. Gupta ◽  
B. Lohani
Keyword(s):  
Augmented Reality ◽  
Point Cloud ◽  
Smart Phone ◽  
Research Problem ◽  
Point Cloud Data ◽  
Registration Method ◽  
Cloud Data ◽  
Augmented Reality System ◽  
The Status ◽  
Intensity Image

Mobile augmented reality system is the next generation technology to visualise 3D real world intelligently. The technology is expanding at a fast pace to upgrade the status of a smart phone to an intelligent device. The research problem identified and presented in the current work is to view actual dimensions of various objects that are captured by a smart phone in real time. The methodology proposed first establishes correspondence between LiDAR point cloud, that are stored in a server, and the image t hat is captured by a mobile. This correspondence is established using the exterior and interior orientation parameters of the mobile camera and the coordinates of LiDAR data points which lie in the viewshed of the mobile camera. A pseudo intensity image is generated using LiDAR points and their intensity. Mobile image and pseudo intensity image are then registered using image registration method SIFT thereby generating a pipeline to locate a point in point cloud corresponding to a point (pixel) on the mobile image. The second part of the method uses point cloud data for computing dimensional information corresponding to the pairs of points selected on mobile image and fetch the dimensions on top of the image. This paper describes all steps of the proposed method. The paper uses an experimental setup to mimic the mobile phone and server system and presents some initial but encouraging results

scholarly journals A Mobile Augmented Reality System for the Real-Time Visualization of Pipes in Point Cloud Data with a Depth Sensor

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 836 ◽  
Author(s):  
Young-Hoon Jin ◽  
In-Tae Hwang ◽  
Won-Hyung Lee
Keyword(s):  
Augmented Reality ◽  
Real Time ◽  
Point Cloud ◽  
Depth Image ◽  
Visualization Method ◽  
Point Cloud Data ◽  
Matching Method ◽  
The Real ◽  
Cloud Data ◽  
Real Time Visualization

Augmented reality (AR) is a useful visualization technology that displays information by adding virtual images to the real world. In AR systems that require three-dimensional information, point cloud data is easy to use after real-time acquisition, however, it is difficult to measure and visualize real-time objects due to the large amount of data and a matching process. In this paper we explored a method of estimating pipes from point cloud data and visualizing them in real-time through augmented reality devices. In general, pipe estimation in a point cloud uses a Hough transform and is performed through a preprocessing process, such as noise filtering, normal estimation, or segmentation. However, there is a disadvantage in that the execution time is slow due to a large amount of computation. Therefore, for the real-time visualization in augmented reality devices, the fast cylinder matching method using random sample consensus (RANSAC) is required. In this paper, we proposed parallel processing, multiple frames, adjustable scale, and error correction for real-time visualization. The real-time visualization method through the augmented reality device obtained a depth image from the sensor and configured a uniform point cloud using a voxel grid algorithm. The constructed data was analyzed according to the fast cylinder matching method using RANSAC. The real-time visualization method through augmented reality devices is expected to be used to identify problems, such as the sagging of pipes, through real-time measurements at plant sites due to the spread of various AR devices.

scholarly journals AN AUTOMATIC EXTRACTION METHOD FOR THE PARAMETERS OF MULTI-LOD BIM MODELS FOR TYPICAL COMPONENTS OF WOODEN ARCHITECTURAL HERITAGE

2019 ◽  
Vol XLII-2/W15 ◽  
pp. 679-685
Author(s):  
H. Liu ◽  
M. Hou ◽  
A. Li ◽  
L. Xie
Keyword(s):  
Song Dynasty ◽  
Extraction Method ◽  
Point Cloud ◽  
Point Clouds ◽  
Model Parameters ◽  
Automatic Extraction ◽  
High Fidelity ◽  
Point Cloud Data ◽  
Architectural Heritage ◽  
Cloud Data

<p><strong>Abstract.</strong> A demand-oriented Building Information Model (BIM) model built using high-fidelity point cloud data can better protect architectural heritage. The multi-level detail (mutli-LoD) parametric model emphasizes the different protection requirements of typical components and the automatic extraction of corresponding parameters of high-fidelity point clouds, which are two related key issues. Taking the typical Chinese wooden architectural heritage as an example, according to different requirements, the multi-LoD principle of typical components is proposed. On this basis, the automatic extraction method of the above parameters is developed, and the key parameters of the method are recommended. In order to solve the above problems, taking the three typical Dou-Gong used in Liao Dynasty and Song Dynasty, including Zhutou Puzuo, Bujian Puzuo and Zhuanjiao Puzuo, as an example, briefly introduced the standardization characteristics of the typical components of the "Yingzao Fashi". Subsequently, the corresponding multiple LoD principles are recommended according to different requirements. Based on this and high-fidelity point cloud data, an automatic extraction method for multi-LoD BIM model parameters for typical components of wooden architectural heritage is proposed.</p>

scholarly journals Improving Plane Fitting Accuracy with Rigorous Error Models of Structured Light-Based RGB-D Sensors

2020 ◽  
Vol 12 (2) ◽  
pp. 320 ◽  
Author(s):  
Yaxin Li ◽  
Wenbin Li ◽  
Walid Darwish ◽  
Shengjun Tang ◽  
Yuling Hu ◽  
...  
Keyword(s):  
Point Cloud ◽  
Error Model ◽  
Equal Weight ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Plane Fitting ◽  
Fitting Method ◽  
The Mean ◽  
Rigorous Error ◽  
Fitting Accuracy

Plane fitting is a fundamental operation for point cloud data processing. Most existing methods for point cloud plane fitting have been developed based on high-quality Lidar data giving equal weight to the point cloud data. In recent years, using low-quality RGB-Depth (RGB-D) sensors to generate 3D models has attracted much attention. However, with low-quality point cloud data, equal weight plane fitting methods are not optimal as the range errors of RGB-D sensors are distance-related. In this paper, we developed an accurate plane fitting method for a structured light (SL)-based RGB-D sensor. First, we derived an error model of a point cloud dataset from the SL-based RGB-D sensor through error propagation from the raw measurement to the point coordinates. A new cost function based on minimizing the radial distances with the derived rigorous error model was then proposed for the random sample consensus (RANSAC)-based plane fitting method. The experimental results demonstrated that our method is robust and practical for different operating ranges and different working conditions. In the experiments, for the operating ranges from 1.23 meters to 4.31 meters, the mean plane angle errors were about one degree, and the mean plane distance errors were less than six centimeters. When the dataset is of a large-depth-measurement scale, the proposed method can significantly improve the plane fitting accuracy, with a plane angle error of 0.5 degrees and a mean distance error of 4.7 cm, compared to 3.8 degrees and 16.8 cm, respectively, from the conventional un-weighted RANSAC method. The experimental results also demonstrate that the proposed method is applicable for different types of SL-based RGB-D sensor. The rigorous error model of the SL-based RGB-D sensor is essential for many applications such as in outlier detection and data authorization. Meanwhile, the precise plane fitting method developed in our research will benefit algorithms based on high-accuracy plane features such as depth calibration, 3D feature-based simultaneous localization and mapping (SLAM), and the generation of indoor building information models (BIMs).

scholarly journals Sensor Fusion Based Pipeline Inspection for the Augmented Reality System

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1325 ◽  
Author(s):  
Kumar ◽  
Patil ◽  
Kang ◽  
Chai
Keyword(s):  
Augmented Reality ◽  
Sensor Fusion ◽  
Point Cloud ◽  
Treatment Plant ◽  
Evaluation Process ◽  
Water Treatment Plant ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Pipeline Inspection ◽  
Augmented Reality System

Augmented reality (AR) systems are becoming next-generation technologies to intelligently visualize the real world in 3D. This research proposes a sensor fusion based pipeline inspection and retrofitting for the AR system, which can be used in pipeline inspection and retrofitting processes in industrial plants. The proposed methodology utilizes a prebuilt 3D point cloud data of the environment, real-time Light Detection and Ranging (LiDAR) scan and image sequence from the camera. First, we estimate the current pose of the sensors platform by matching the LiDAR scan and the prebuilt point cloud data from the current pose prebuilt point cloud data augmented on to the camera image by utilizing the LiDAR and camera calibration parameters. Next, based on the user selection in the augmented view, geometric parameters of a pipe are estimated. In addition to pipe parameter estimation, retrofitting in the existing plant using augmented scene are illustrated. Finally, step-by-step procedure of the proposed method was experimentally verified at a water treatment plant. Result shows that the integration of AR with building information modelling (BIM) greatly benefits the post-occupancy evaluation process or pre-retrofitting and renovation process for identifying, evaluating, and updating the geometric specifications of a construction environment.

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

2020 ◽  
Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Point Clouds ◽  
Optical Image ◽  
Point Cloud Data ◽  
Feature Points ◽  
Lidar System ◽  
Registration Accuracy ◽  
Cloud Data ◽  
Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

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