Obstacle prediction for automated guided vehicles based on point clouds measured by a tilted lidar sensor

Zoltan Rozsa, Tamas Sziranyi

Research output: Contribution to journalArticle

16 Citations (Scopus)


Environment analysis of automatic vehicles needs the detection from 3-D point cloud information. This paper addresses this task when only partial scanning data are available. Our method develops the detection capabilities of autonomous vehicles equipped with 3-D range sensors for navigation purposes. In industrial practice, the safety scanners of automated guided vehicles (AGVs) and a localization technology provide an additional possibility to gain 3-D point clouds from planar contour points or low vertical resolution. Based on this data and a suitable evaluation algorithm, intelligence of vehicles can be significantly increased without the need for installation of additional sensors. In this paper, we propose a solution for an obstacle categorization problem for partial point clouds without shape modeling. The approach is tested for a known database, as well as for real-life scenarios. In case of AGVs, real-time run is provided by on-board computers of usual complexity.

Original languageEnglish
Article number8283563
Pages (from-to)2708-2720
Number of pages13
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number8
Publication statusPublished - Aug 2018


  • automated guided vehicles
  • autonomous vehicle
  • bag of features
  • keypoint detection
  • object recognition
  • point cloud

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

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