A dynamic vision algorithm to locate a vehicle on a nonstructured road

被引:5
|
作者
Aufrère, R [1 ]
Chapuis, R [1 ]
Chausse, F [1 ]
机构
[1] Univ Blaise Pascal, LASMEA, CNRS, UMR 6602, F-63177 Aubiere, France
关键词
autonomous navigation; computer vision; road-following; lane boundary detection; pixel classification;
D O I
10.1177/02783640022066941
中图分类号
TP24 [机器人技术];
学科分类号
080202 ; 1405 ;
摘要
In this article, we present a method of nonmarked road following that is based on images coming from an onboard monochromatic camera. The principle is based first on a segmentation stage that makes it possible to locate the road area in the image, managing, if possible, the shadows on the roadway. The method is original since the algorithm must be running day as well as night (infrared camera) so it does not use color images. Furthermore, a single constant threshold is used whatever the analyzed sequence. Then, a localization stage estimates the vehicle's location on the roadway. The estimate of the parameters L (road width) and or (camera inclination angle) (assumed known and constant for certain existing approaches) ensures a greater robustness of the other estimated parameters. Finally, a filtering stage is applied onto the previous data and predicts the position of the vehicle in the next image. Results ave shown for each stage on both a normal nonmarked road and a forest lane sequence. The computational times are very low and will permit a real-time implementation on an experimental vehicle.
引用
收藏
页码:411 / 423
页数:13
相关论文
共 50 条
  • [41] Vehicle detection and tracking for visual understanding of road environments
    de la Escalera, Arturo
    Maria Armingol, Jose
    ROBOTICA, 2010, 28 : 847 - 860
  • [42] Application of Computer Vision for Estimation of Moving Vehicle Weight
    Feng, Maria Q.
    Leung, Ryan Y.
    IEEE SENSORS JOURNAL, 2021, 21 (10) : 11588 - 11597
  • [43] TRODO: A public vehicle odometers dataset for computer vision
    Mouheb, Kaouther
    Yurekli, Ali
    Yilmazel, Burcu
    DATA IN BRIEF, 2021, 38
  • [44] Altitude control of an underwater vehicle based on computer vision
    Rodrigues, Pedro M.
    Cruz, Nuno A.
    Pinto, Andry M.
    OCEANS 2018 MTS/IEEE CHARLESTON, 2018,
  • [45] A Computer Vision Based Vehicle Detection and Counting System
    Seenouvong, Nilakorn
    Watchareeruetai, Ukrit
    Nuthong, Chaiwat
    Khongsomboon, Khamphong
    Ohnishi, Noboru
    2016 8TH INTERNATIONAL CONFERENCE ON KNOWLEDGE AND SMART TECHNOLOGY (KST), 2016, : 224 - 227
  • [46] A robust landmark-based system for vehicle location using low-bandwidth vision
    Lin, LJ
    Hancock, TR
    Judd, JS
    ROBOTICS AND AUTONOMOUS SYSTEMS, 1998, 25 (1-2) : 19 - 32
  • [47] Opportunities and Challenges in Vehicle Tracking: A Computer Vision-Based Vehicle Tracking System
    Atousa Zarindast
    Anuj Sharma
    Data Science for Transportation, 2023, 5 (1):
  • [48] Computer vision approaches for vehicle sideslip angle estimation
    Serena, Leonardo
    Lenzo, Basilio
    Bruschetta, Mattia
    de Castro, Ricardo
    2023 IEEE INTERNATIONAL WORKSHOP ON METROLOGY FOR AUTOMOTIVE, METROAUTOMOTIVE, 2023, : 181 - 186
  • [49] An effective and efficient approximate two-dimensional dynamic programming algorithm for supporting advanced computer vision applications
    Cuzzocrea, Alfredo
    Mumolo, Enzo
    Grasso, Giorgio Mario
    Vercelli, Gianni
    JOURNAL OF VISUAL LANGUAGES AND COMPUTING, 2017, 42 : 13 - 22
  • [50] Vision-based Vehicle Detection and Distance Estimation
    Qiao, Donghao
    Zulkernine, Farhana
    2020 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI), 2020, : 2836 - 2842