• Title/Summary/Keyword: Cloud point extraction

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Automated Construction of IndoorGML Data Using Point Cloud (포인트 클라우드를 이용한 IndoorGML 데이터의 자동적 구축)

  • Kim, Sung-Hwan;Li, Ki-Joune
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.38 no.6
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    • pp.611-622
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    • 2020
  • As the advancement of technologies on indoor positioning systems and measuring devices such as LiDAR (Light Detection And Ranging) and cameras, the demands on analyzing and searching indoor spaces and visualization services via virtual and augmented reality have rapidly increasing. To this end, it is necessary to model 3D objects from measured data from real-world structures. In addition, it is important to store these structured data in standardized formats to improve the applicability and interoperability. In this paper, we propose a method to construct IndoorGML data, which is an international standard for indoor modeling, from point cloud data acquired from LiDAR sensors. After examining considerations that should be addressed in IndoorGML data, we present a construction method, which consists of free space extraction and connectivity detection processes. With experimental results, we demonstrate that the proposed method can effectively reconstruct the 3D model from point cloud.

Point Cloud-based Automated Building Tilt Measurement (포인트 클라우드 기반 건축물 기울기 측정 자동화)

  • Dayoung Yu;Chaeeun Lee;Sung-Han Sim
    • Journal of the Korea institute for structural maintenance and inspection
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    • v.27 no.5
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    • pp.84-88
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    • 2023
  • This study proposes an automated tilt measurement method using point cloud for buildings. The proposed method consists of two main steps: 1) exterior wall plane extraction, and 2) edge estimation and angle calculation. To validate the performance of the proposed method, the algorithm is applied to a target building, of which the estimated tilt values are compared with those obtained from a total station, a commonly used tool for tilt measurement. The result shows that most estimated tilt values are within the maximum and minimum ranges of the total station measurement, suggesting that the proposed algorithm provides sufficient measurement accuracy. Furthermore, the proposed method is shown to be automated and reliable as well as free from human-induced errors compared to the total station.

Development of Digital Surface Model and Feature Extraction by Integrating Laser Scanner and CCD sensor

  • Nagai, Masahiko;Shibasaki, Ryosuke;Zhao, Huijing;Manandhar, Dinesh
    • Proceedings of the KSRS Conference
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    • 2003.11a
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    • pp.859-861
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    • 2003
  • In order to present a space in details, it is indispensable to acquire 3D shape and texture simultaneously from the same platform. 3D shape is acquired by Laser Scanner as point cloud data, and texture is acquired by CCD sensor. Positioning data is acquired by IMU (Inertial Measurement Unit). All the sensors and equipments are assembled on a hand-trolley. In this research, a method of integrating the 3D shape and texture for automated construction of Digital Surface Model is developed. This Digital Surface Model is applied for efficient feature extraction. More detailed extraction is possible , because 3D Digital Surface Model has both 3D shape and texture information.

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Adaptive Cloud Offloading of Augmented Reality Applications on Smart Devices for Minimum Energy Consumption

  • Chung, Jong-Moon;Park, Yong-Suk;Park, Jong-Hong;Cho, HyoungJun
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.9 no.8
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    • pp.3090-3102
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    • 2015
  • The accuracy of an augmented reality (AR) application is highly dependent on the resolution of the object's image and the device's computational processing capability. Naturally, a mobile smart device equipped with a high-resolution camera becomes the best platform for portable AR services. AR applications require significant energy consumption and very fast response time, which are big burdens to the smart device. However, there are very few ways to overcome these burdens. Computation offloading via mobile cloud computing has the potential to provide energy savings and enhance the performance of applications executed on smart devices. Therefore, in this paper, adaptive mobile computation offloading of mobile AR applications is considered in order to determine optimal offloading points that satisfy the required quality of experience (QoE) while consuming minimum energy of the smart device. AR feature extraction based on SURF algorithm is partitioned into sub-stages in order to determine the optimal AR cloud computational offloading point based on conditions of the smart device, wireless and wired networks, and AR service cloud servers. Tradeoffs in energy savings and processing time are explored also taking network congestion and server load conditions into account.

A Study on Point Cloud Generation Method from UAV Image Using Incremental Bundle Adjustment and Stereo Image Matching Technique (Incremental Bundle Adjustment와 스테레오 영상 정합 기법을 적용한 무인항공기 영상에서의 포인트 클라우드 생성방안 연구)

  • Rhee, Sooahm;Hwang, Yunhyuk;Kim, Soohyeon
    • Korean Journal of Remote Sensing
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    • v.34 no.6_1
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    • pp.941-951
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    • 2018
  • Utilization and demand of UAV (unmanned aerial vehicle) for the generation of 3D city model are increasing. In this study, we performed an experiment to adjustment position/orientation of UAV with incomplete attitude information and to extract point cloud data. In order to correct the attitude of the UAV, the rotation angle was calculated by using the continuous position information of UAV movements. Based on this, the corrected position/orientation information was obtained by applying IBA (Incremental Bundle Adjustment) based on photogrammetry. Each pair was transformed into an epipolar image, and the MDR (Multi-Dimensional Relaxation) technique was applied to obtain high precision DSM. Each extracted pair is aggregated and output in the form of a single point cloud or DSM. Using the DJI inspire1 and Phantom4 images, we can confirm that the point cloud can be extracted which expresses the railing of the building clearly. In the future, research will be conducted on improving the matching performance and establishing sensor models of oblique images. After that, we will continue the image processing technology for the generation of the 3D city model through the study of the extraction of 3D cloud It should be developed.

Automatic Extraction of River Levee Slope Using MMS Point Cloud Data (MMS 포인트 클라우드를 활용한 하천제방 경사도 자동 추출에 관한 연구)

  • Kim, Cheolhwan;Lee, Jisang;Choi, Wonjun;Kim, Wondae;Sohn, Hong-Gyoo
    • Korean Journal of Remote Sensing
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    • v.37 no.5_3
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    • pp.1425-1434
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    • 2021
  • Continuous and periodic data acquisition must be preceded to maintain and manage the river facilities effectively. Adapting the existing general facilities methods, which include river surveying methods such as terrestrial laser scanners, total stations, and Global Navigation Satellite System (GNSS), has limitation in terms of its costs, manpower, and times to acquire spatial information since the river facilities are distributed across the wide and long area. On the other hand, the Mobile Mapping System (MMS) has comparative advantage in acquiring the data of river facilities since it constructs three-dimensional spatial information while moving. By using the MMS, 184,646,009 points could be attained for Anyang stream with a length of 4 kilometers only in 20 minutes. Levee points were divided at intervals of 10 meters so that about 378 levee cross sections were generated. In addition, the waterside maximum and average slope could be automatically calculated by separating slope plane form levee point cloud, and the accuracy of RMSE was confirmed by comparing with manually calculated slope. The reference slope was calculated manually by plotting point cloud of levee slope plane and selecting two points that use location information when calculating the slope. Also, as a result of comparing the water side slope with slope standard in basic river plan for Anyang stream, it is confirmed that inspecting the river facilities with the MMS point cloud is highly recommended than the existing river survey.

Valve Model Extraction from Noisy 3-D Point Cloud Data (잡음이 있는 3차원 점군 정보에서의 밸브 모형 추출)

  • Oh, Ki-Won;Choi, Kang-Sun
    • Proceedings of the Korea Information Processing Society Conference
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    • 2015.04a
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    • pp.945-946
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    • 2015
  • Laser Range Finder를 이용하여 생성한 3차원 점군 정보는 단면적인 부분만 볼 수 있으며, 잡음이 포함되어 작은 물체를 추출하는데 많은 영향이 생긴다. 이러한 잡음이 있는 3차원 점군 정보 사이에서 밸브의 중심의 위치에 대한 추가적인 입력을 받아 원환체, 원통, 평면의 정보를 복합적으로 포함하고 있는 밸브의 모델을 추출한다.

Key Management for Secure Internet of Things(IoT) Data in Cloud Computing (클라우드 컴퓨팅에서 안전한 사물인터넷 데이터를 위한 키 관리)

  • Sung, Soon-hwa
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.27 no.2
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    • pp.353-360
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    • 2017
  • The Internet of Things(IoT) security has more need than a technical problem as it needs series of regulations and faultless security system for common purposes. So, this study proposes an efficient key management in order that can be trusted IoT data in cloud computing. In contrast with a key distribution center of existing sensor networks, the proposed a federation key management of cloud proxy key server is not central point of administration and enables an active key recovery and update. The proposed key management is not a method of predetermined secret keys but sharing key information of a cloud proxy key server in autonomous cloud, which can reduce key generation and space complexity. In addition, In contrast with previous IoT key researches, a federation key of cloud proxy key server provides an extraction ability from meaningful information while moving data.

Extraction and Utilization of DEM based on UAV Photogrammetry for Flood Trace Investigation and Flood Prediction (침수흔적조사를 위한 UAV 사진측량 기반 DEM의 추출 및 활용)

  • Jung-Sik PARK;Yong-Jin CHOI;Jin-Duk LEE
    • Journal of the Korean Association of Geographic Information Studies
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    • v.26 no.4
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    • pp.237-250
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    • 2023
  • Orthophotos and DEMs were generated by UAV-based aerial photogrammetry and an attempt was made to apply them to detailed investigations for the production of flood traces. The cultivated area located in Goa-eup, Gumi, where the embankment collapsed and inundated inundation occurred due to the impact of 6th Typhoon Sanba in 2012, was selected as rhe target area. To obtain optimal accuracy of UAV photogrammetry performance, the UAV images were taken under the optimal placement of 19 GCPs and then point cloud, DEM, and orthoimages were generated through image processing using Pix4Dmapper software. After applying CloudCompare's CSF Filtering to separate the point cloud into ground elements and non-ground elements, a finally corrected DEM was created using only non-ground elements in GRASS GIS software. The flood level and flood depth data extracted from the final generated DEM were compared and presented with the flood level and flood depth data from existing data as of 2012 provided through the public data portal site of the Korea Land and Geospatial Informatix Corporation(LX).

Semi-automatic Extraction of 3D Building Boundary Using DSM from Stereo Images Matching (영상 매칭으로 생성된 DSM을 이용한 반자동 3차원 건물 외곽선 추출 기법 개발)

  • Kim, Soohyeon;Rhee, Sooahm
    • Korean Journal of Remote Sensing
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    • v.34 no.6_1
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    • pp.1067-1087
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    • 2018
  • In a study for LiDAR data based building boundary extraction, usually dense point cloud was used to cluster building rooftop area and extract building outline. However, when we used DSM generated from stereo image matching to extract building boundary, it is not trivial to cluster building roof top area automatically due to outliers and large holes of point cloud. Thus, we propose a technique to extract building boundary semi-automatically from the DSM created from stereo images. The technique consists of watershed segmentation for using user input as markers and recursive MBR algorithm. Since the proposed method only inputs simple marker information that represents building areas within the DSM, it can create building boundary efficiently by minimizing user input.