Development of Point-cloud Processing Algorithm for Self-Driving Challenges

被引:0
|
作者
Unger, Miklos [1 ]
Horvath, Erno [1 ]
Koros, Peter [1 ]
机构
[1] Szechenyi Istvan Univ, Res Ctr Vehicle Ind, Gyor, Hungary
关键词
self-driving; point-cloud; laserscanner; LIDAR; proceeding; filter;
D O I
10.1109/ines49302.2020.9147201
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The paper proposes an own-developed point-cloud processing algorithm which was developed for the Autonomous Urban Concept competition organized by Shell. The approach does not intend to solve general-purpose object recognition and tracking, although the methodologies presented can be used as general solutions. Our approach will be presented in comprehensive manner, the challenges and solutions will be detailed. Also, the dysfunctional ideas will be listed, and alternative workarounds will be presented as recommendations too. As verification of the algorithm, both simulation and real-world measurements will be presented. For the sake of research and open source, we share datasets and necessary information publicly.
引用
收藏
页码:91 / 95
页数:5
相关论文
共 50 条
  • [21] Self-driving Deep Learning System based on Depth Image Based Rendering and LiDAR Point Cloud
    Lin, Guo-Han
    Chang, Chun-Hsiang
    Chung, Ming-Chun
    Fan, Yu-Cheng
    [J]. 2020 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS - TAIWAN (ICCE-TAIWAN), 2020,
  • [22] Perception Algorithm of Position and Attitude for Self-Driving Rollers
    Xie H.
    Liu Y.
    Yan L.
    [J]. Tianjin Daxue Xuebao (Ziran Kexue yu Gongcheng Jishu Ban)/Journal of Tianjin University Science and Technology, 2021, 54 (06): : 551 - 560
  • [23] Point-Cloud Processing Using Modified Rodrigues Parameters for Relative Navigation
    Bercovici, Benjamin
    McMahon, Jay W.
    [J]. JOURNAL OF GUIDANCE CONTROL AND DYNAMICS, 2017, 40 (12) : 3167 - 3179
  • [24] The development of valid CAD models from point-cloud data
    Claustre, T
    Smith, G
    [J]. ADVANCES IN MANUFACTURING TECHNOLOGY - XV, 2001, : 219 - 224
  • [25] THREE-DIMENSIONAL POINT-CLOUD REGISTRATION USING A GENETIC ALGORITHM AND THE ITERATIVE CLOSEST POINT ALGORITHM
    Torres, D.
    Cuevas, F. J.
    [J]. ECTA 2011/FCTA 2011: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON EVOLUTIONARY COMPUTATION THEORY AND APPLICATIONS AND INTERNATIONAL CONFERENCE ON FUZZY COMPUTATION THEORY AND APPLICATIONS, 2011, : 547 - 552
  • [26] Self-Supervised Learning for Point-Cloud Classification by a Multigrid Autoencoder
    Zhai, Ruifeng
    Song, Junfeng
    Hou, Shuzhao
    Gao, Fengli
    Li, Xueyan
    [J]. SENSORS, 2022, 22 (21)
  • [27] A GMM BASED ALGORITHM TO GENERATE POINT-CLOUD AND ITS APPLICATION TO NEUROIMAGING
    Yang, Liu
    Chakraborty, Rudrasis
    [J]. 2020 IEEE 17TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING WORKSHOPS (IEEE ISBI WORKSHOPS 2020), 2020,
  • [28] SELF-DRIVING CAR USING LIDAR SENSING AND IMAGE PROCESSING
    Memon, Sidra
    Ahmed, Muhammad
    Narejo, Sanam
    Baig, Umer Ahmed
    Chowdry, Bhawani Shankar
    Anjum, M. Rizwan
    [J]. INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING, 2020, 13 (02): : 77 - 88
  • [29] Implicit Autoencoder for Point-Cloud Self-Supervised Representation Learning
    Yan, Siming
    Yang, Zhenpei
    Li, Haoxiang
    Song, Chen
    Guan, Li
    Kang, Hao
    Hua, Gang
    Huang, Qixing
    [J]. 2023 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2023), 2023, : 14484 - 14496
  • [30] Self-driving laboratories: A paradigm shift in nanomedicine development
    Hickman, Riley J.
    Bannigan, Pauric
    Bao, Zeqing
    Aspuru-Guzik, Alan
    Allen, Christine
    [J]. MATTER, 2023, 6 (04) : 1071 - 1081