Obstacle Prediction for Automated Guided Vehicles Based on Point Clouds Measured by a Tilted LIDAR Sensor

Zoltan Rozsa, T. Szirányi

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

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
JournalIEEE Transactions on Intelligent Transportation Systems
DOIs
Publication statusAccepted/In press - Feb 6 2018

Fingerprint

Sensors
Printed circuit boards
Navigation
Scanning

Keywords

  • automated guided vehicles
  • autonomous vehicle
  • Autonomous vehicles
  • bag of features.
  • keypoint detection
  • Laser radar
  • LIDAR
  • Object recognition
  • object recognition
  • point cloud
  • Robot sensing systems
  • Shape
  • Three-dimensional displays
  • Two dimensional displays

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

Cite this

@article{44a3efb960884e13a6d883787ac77c12,
title = "Obstacle Prediction for Automated Guided Vehicles Based on Point Clouds Measured by a Tilted LIDAR Sensor",
abstract = "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.",
keywords = "automated guided vehicles, autonomous vehicle, Autonomous vehicles, bag of features., keypoint detection, Laser radar, LIDAR, Object recognition, object recognition, point cloud, Robot sensing systems, Shape, Three-dimensional displays, Two dimensional displays",
author = "Zoltan Rozsa and T. Szir{\'a}nyi",
year = "2018",
month = "2",
day = "6",
doi = "10.1109/TITS.2018.2790264",
language = "English",
journal = "IEEE Transactions on Intelligent Transportation Systems",
issn = "1524-9050",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - JOUR

T1 - Obstacle Prediction for Automated Guided Vehicles Based on Point Clouds Measured by a Tilted LIDAR Sensor

AU - Rozsa, Zoltan

AU - Szirányi, T.

PY - 2018/2/6

Y1 - 2018/2/6

N2 - 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.

AB - 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.

KW - automated guided vehicles

KW - autonomous vehicle

KW - Autonomous vehicles

KW - bag of features.

KW - keypoint detection

KW - Laser radar

KW - LIDAR

KW - Object recognition

KW - object recognition

KW - point cloud

KW - Robot sensing systems

KW - Shape

KW - Three-dimensional displays

KW - Two dimensional displays

UR - http://www.scopus.com/inward/record.url?scp=85041501357&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85041501357&partnerID=8YFLogxK

U2 - 10.1109/TITS.2018.2790264

DO - 10.1109/TITS.2018.2790264

M3 - Article

AN - SCOPUS:85041501357

JO - IEEE Transactions on Intelligent Transportation Systems

JF - IEEE Transactions on Intelligent Transportation Systems

SN - 1524-9050

ER -