A Study on the Improvement of UAV based 3D Point Cloud Spatial Object Location Accuracy using Road Information

被引:2
|
作者
Lee, Jaehee [1 ]
Kang, Jihun [1 ]
Lee, Sewon [1 ]
机构
[1] Spatial Informat Res Inst, Seoul, South Korea
关键词
UAV; 3D point cloud; location accuracy; transform matrix; 3D spatial object;
D O I
10.7780/kjrs.2019.35.5.1.7
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
Precision positioning is necessary for various use of high-resolution UAV images. Basically, GCP is used for this purpose, but in case of emergency situations or difficulty in selecting GCPs, the data shall be obtained without GCPs. This study proposed a method of improving positional accuracy for x, y coordinate of UAV based 3 dimensional point cloud data generated without GCPs. Road vector file by the public data (Open Data Portal) was used as reference data for improving location accuracy. The geometric correction of the 2 dimensional ortho-mosaic image was first performed and the transform matrix produced in this process was adopted to apply to the 3 dimensional point cloud data. The straight distance difference of 3454 m before the correction was reduced to 1.21 m after the correction. By confirming that it is possible to improve the location accuracy of UAV images acquired without GCPs, it is expected to expand the scope of use of 3 dimensional spatial objects generated from point cloud by enabling connection and compatibility with other spatial information data.
引用
收藏
页码:705 / 714
页数:10
相关论文
共 50 条
  • [21] Exploiting More Information in Sparse Point Cloud for 3D Single Object Tracking
    Cui, Yubo
    Shan, Jiayao
    Gu, Zuoxu
    Li, Zhiheng
    Fang, Zheng
    [J]. IEEE ROBOTICS AND AUTOMATION LETTERS, 2022, 7 (04): : 11926 - 11933
  • [22] 3D Characterization of Sorghum Panicles Using a 3D Point Cloud Derived from UAV Imagery
    Chang, Anjin
    Jung, Jinha
    Yeom, Junho
    Landivar, Juan
    [J]. REMOTE SENSING, 2021, 13 (02) : 1 - 10
  • [23] CenterTransFuser: radar point cloud and visual information fusion for 3D object detection
    Li, Yan
    Zeng, Kai
    Shen, Tao
    [J]. EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING, 2023, 2023 (01)
  • [24] ASCNet: 3D object detection from point cloud based on adaptive spatial context features q
    Tong, Guofeng
    Peng, Hao
    Shao, Yuyuan
    Yin, Qijun
    Li, Zheng
    [J]. NEUROCOMPUTING, 2022, 475 : 89 - 101
  • [25] PointDCCNet: 3D Object Categorization Network using Point Cloud Decomposition
    Katageri, Siddharth
    Kulmi, Sameer
    Tabib, Ramesh Ashok
    Mudenagudi, Uma
    [J]. 2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS, CVPRW 2021, 2021, : 2200 - 2208
  • [26] 3D Point Cloud Processing Using Spin Images for Object Detection
    Ligon, Jason
    Bein, Doina
    Ly, Phillip
    Onesto, Brian
    [J]. 2018 IEEE 8TH ANNUAL COMPUTING AND COMMUNICATION WORKSHOP AND CONFERENCE (CCWC), 2018, : 731 - 736
  • [27] Object Proposal Using 3D Point Cloud for DRC-HUBO
    Shin, Seunghak
    Shim, Inwook
    Jung, Jiyung
    Bok, Yunsu
    Oh, Jun-Ho
    Kweon, In So
    [J]. 2016 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2016), 2016, : 590 - 597
  • [28] Point Cloud Object Recognition using 3D Convolutional Neural Networks
    Soares, Marcelo Borghetti
    Wermter, Stefan
    [J]. 2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2018,
  • [29] 3D object detection based on point cloud in automatic driving scene
    Hai-Sheng Li
    Yan-Ling Lu
    [J]. Multimedia Tools and Applications, 2024, 83 : 13029 - 13044
  • [30] 3D Object Recognition Method Based on Point Cloud Sequential Coding
    Dong, Shuai
    Ren, Li
    Zou, Kun
    Li, Wensheng
    [J]. ICMLC 2020: 2020 12TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND COMPUTING, 2018, : 297 - 300