Construction of 3D Environment Models by Fusing Ground and Aerial Lidar Point Cloud Data

被引:1
|
作者
Langerwisch, Marco [1 ]
Kraemer, Marc Steven [2 ]
Kuhnert, Klaus-Dieter [2 ]
Wagner, Bernardo [1 ]
机构
[1] Leibniz Univ Hannover, Real Time Syst Grp RTS, Appelstr 9A, D-30167 Hannover, Germany
[2] Univ Siegen, Inst Real Time Learning Syst EZLS, Holderlinstr 3, D-57068 Siegen, Germany
来源
INTELLIGENT AUTONOMOUS SYSTEMS 13 | 2016年 / 302卷
关键词
3D lidar point clouds; unmanned aerial vehicles; sensor data fusion; octomaps; 6DoF SLAM;
D O I
10.1007/978-3-319-08338-4_35
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
A lot of research work deals with the building of 3D environment models, e.g. by lidar-based 6D SLAM on ground vehicles. Because these single vehicle approaches always are afflicted by partial occlusion of the environment, we propose to fuse point cloud data taken by ground and aerial vehicles. Therefore, we use manually steered ground and aerial vehicles equipped with localization sensors and laser scanners to record point cloud data. The point cloud data is fused predominantly by existing state-of-the-art algorithms and data formats in ROS. Finally, Octomaps are calculated as common environment models. Two real world experiments in structured and unstructured outdoor environments are presented. The resulting point clouds and maps are evaluated qualitatively and quantitatively.
引用
收藏
页码:473 / 485
页数:13
相关论文
共 50 条
  • [1] Aerial Lidar Point Cloud Voxelization with its 3D Ground Filtering Application
    Wang, Liying
    Xu, Yan
    Li, Yu
    PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, 2017, 83 (02): : 95 - 107
  • [2] 3D City Models completion by Fusing Lidar and Image Data
    Grammatikopoulos, L.
    Kalisperakis, I.
    Petsa, E.
    Stentoumis, C.
    VIDEOMETRICS, RANGE IMAGING, AND APPLICATIONS XIII, 2015, 9528
  • [3] The Ground Segmentation of 3D LIDAR Point Cloud with the Optimized Region Merging
    Na, Kiin
    Byun, Jaemin
    Roh, Myongchan
    Seo, Bumsu
    2013 INTERNATIONAL CONFERENCE ON CONNECTED VEHICLES AND EXPO (ICCVE), 2013, : 445 - 450
  • [4] Uniform Grid Upsampling of 3D LiDAR Point Cloud Data
    Gurram, Prudhvi
    Hu, Shuowen
    Chan, Alex
    THREE-DIMENSIONAL IMAGE PROCESSING (3DIP) AND APPLICATIONS 2013, 2013, 8650
  • [5] 3D campus modeling using LiDAR point cloud data
    Kawata, Yoshiyuki
    Yoshii, Satoshi
    Funatsu, Yukihiro
    Takemata, Kazuya
    EARTH RESOURCES AND ENVIRONMENTAL REMOTE SENSING/GIS APPLICATIONS III, 2012, 8538
  • [6] Accelerated Generative Models for 3D Point Cloud Data
    Ben Eckart
    Kim, Kihwan
    Troccoli, Alejandro
    Kelly, Alonzo
    Kautz, Jan
    2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, : 5497 - 5505
  • [7] The Algorithm of Obtaining 3D Point Cloud Data in Ground Fissure
    Xu, Mingxia
    Li, Bin
    Feng, Mingxiang
    Xu, Yonglong
    Wang, Guanhu
    MECHATRONICS ENGINEERING, COMPUTING AND INFORMATION TECHNOLOGY, 2014, 556-562 : 3575 - +
  • [8] LIDAR Point Cloud Data Extraction and Establishment of 3D Modeling of Buildings
    Zhang, Yujuan
    Li, Xiuhai
    Wang, Qiang
    Liu, Jiang
    Liang, Xin
    Li, Dan
    Ni, Chundi
    Liu, Yan
    5TH ANNUAL INTERNATIONAL CONFERENCE ON MATERIAL SCIENCE AND ENVIRONMENTAL ENGINEERING (MSEE2017), 2018, 301
  • [9] Fusing Correspondenceless 3D Point Distribution Models
    Pereanez, Marco
    Lekadir, Karim
    Butakoff, Constantine
    Hoogendoorn, Corne
    Frangi, Alejandro
    MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION (MICCAI 2013), PT I, 2013, 8149 : 251 - 258
  • [10] 3D modelling method and application to a digital campus by fusing point cloud data and image data
    Yuanyuan, F. E. N. G.
    Hao, L. . I.
    Chaokui, L. I.
    Jun, C. . H. E. N.
    HELIYON, 2024, 10 (17)