3D Sensor-Based Obstacle Detection Comparing Octrees and Point clouds Using CUDA

被引:3
|
作者
Kaldestad, K. B. [1 ]
Hovland, G. [1 ]
Anisi, D. A. [2 ]
机构
[1] Univ Agder, Dept Engn, Fac Technol & Sci, N-4898 Grimstad, Norway
[2] ABB AS, Strateg R&D Oil Gas & Petrochem, N-0666 Oslo, Norway
关键词
Collision Detection; Industrial Robot; Hidden Markov Model; Expert System;
D O I
10.4173/mic.2012.4.1
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper presents adaptable methods for achieving fast collision detection using the GPU and Nvidia CUDA together with Octrees. Earlier related work have focused on serial methods, while this paper presents a parallel solution which shows that there is a great increase in time if the number of operations is large. Two different models of the environment and the industrial robot are presented, the first is Octrees at different resolutions, the second is a point cloud representation. The relative merits of the two different world model representations are shown. In particular, the experimental results show the potential of adapting the resolution of the robot and environment models to the task at hand.
引用
收藏
页码:123 / 130
页数:8
相关论文
共 50 条
  • [31] Multiscale Saliency Detection for Colored 3D Point Clouds Based on Random Walk
    Jeong, Se-Won
    Yun, Jae-Seong
    Sim, Jae-Young
    2020 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA ASC), 2020, : 1193 - 1200
  • [32] Towards 3D Indoor Cadastre Based on Change Detection from Point Clouds
    Koeva, Mila
    Nikoohemat, Shayan
    Elberink, Sander Oude
    Morales, Javier
    Lemmen, Christiaan
    Zevenbergen, Jaap
    REMOTE SENSING, 2019, 11 (17)
  • [33] Traffic participants classification based on 3D radio detection and ranging point clouds
    Bai, Jie
    Li, Sen
    Tan, Bin
    Zheng, Lianqing
    Huang, Libo
    Dong, Lianfei
    IET RADAR SONAR AND NAVIGATION, 2022, 16 (02): : 278 - 290
  • [34] ADAPTIVE FUSION-BASED 3D KEYPOINT DETECTION FOR RGB POINT CLOUDS
    Iqbal, Muhammad Zafar
    Bobkov, Dmytro
    Steinbach, Eckehard
    2019 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2019, : 3711 - 3715
  • [35] A review of road 3D modeling based on light detection and ranging point clouds
    Yu, Bin
    Wang, Yuchen
    Chen, Qihang
    Chen, Xiaoyang
    Zhang, Yuqin
    Luan, Kaiyue
    Ren, Xiaole
    Journal of Road Engineering, 2024, 4 (04) : 386 - 398
  • [36] A Survey on Deep Learning Based Segmentation, Detection and Classification for 3D Point Clouds
    Vinodkumar, Prasoon Kumar
    Karabulut, Dogus
    Avots, Egils
    Ozcinar, Cagri
    Anbarjafari, Gholamreza
    ENTROPY, 2023, 25 (04)
  • [37] Rock joint detection from 3D point clouds based on colour space
    Ge, Yunfeng
    Cao, Bei
    Chen, Qian
    Wang, Yu
    QUARTERLY JOURNAL OF ENGINEERING GEOLOGY AND HYDROGEOLOGY, 2023, 56 (04)
  • [38] Clusterformer: Cluster-based Transformer for 3D Object Detection in Point Clouds
    Pei, Yu
    Zhao, Xian
    Li, Hao
    Ma, Jingyuan
    Zhang, Jingwei
    Pu, Shiliang
    2023 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION, ICCV, 2023, : 6641 - 6650
  • [39] Registration of 3D point clouds using a local descriptor based on grid point normal
    Wang, Jiang
    Wu, Bin
    Kang, Jiehu
    APPLIED OPTICS, 2021, 60 (28) : 8818 - 8828
  • [40] Obstacle detection based on point clouds in application of agricultural navigation
    Ji, Changying
    Shen, Ziyao
    Gu, Baoxing
    Tian, Guangzhao
    Zhang, Jie
    Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering, 2015, 31 (07): : 173 - 179