Efficient Visual Odometry and Mapping for Unmanned Aerial Vehicle Using ARM-based Stereo Vision Pre-Processing System

Changhong, Fu and Carrio Fernández, Adrián and Campoy Cervera, Pascual (2015). Efficient Visual Odometry and Mapping for Unmanned Aerial Vehicle Using ARM-based Stereo Vision Pre-Processing System. In: "2015 International Conference on Unmanned Aircraft Systems (ICUAS)", June 9-12, 2015, Denver, Colorado, USA. ISBN 978-1-4799-6009-5. pp. 957-962.

Description

Title: Efficient Visual Odometry and Mapping for Unmanned Aerial Vehicle Using ARM-based Stereo Vision Pre-Processing System
Author/s:
  • Changhong, Fu
  • Carrio Fernández, Adrián
  • Campoy Cervera, Pascual
Item Type: Presentation at Congress or Conference (Article)
Event Title: 2015 International Conference on Unmanned Aircraft Systems (ICUAS)
Event Dates: June 9-12, 2015
Event Location: Denver, Colorado, USA
Title of Book: 2015 International Conference on Unmanned Aircraft Systems (ICUAS)
Date: 2015
ISBN: 978-1-4799-6009-5
Subjects:
Faculty: E.T.S.I. Industriales (UPM)
Department: Automática, Ingeniería Electrónica e Informática Industrial [hasta 2014]
UPM's Research Group: Computer Vision Group (CVG)
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Visual odometry and mapping methods can provide accurate navigation and comprehensive environment (obstacle) information for autonomous flights of Unmanned Aerial Vehicle (UAV) in GPS-denied cluttered environments. This work presents a new light small-scale low-cost ARM-based stereo vision pre-processing system, which not only is used as onboard sensor to continuously estimate 6-DOF UAV pose, but also as onboard assistant computer to pre-process visual information, thereby saving more computational capability for the onboard host computer of the UAV to conduct other tasks. The visual odometry is done by one plugin specifically developed for this new system with a fixed baseline (12cm). In addition, the preprocessed infromation from this new system are sent via a Gigabit Ethernet cable to the onboard host computer of UAV for real-time environment reconstruction and obstacle detection with a octree-based 3D occupancy grid mapping approach, i.e. OctoMap. The visual algorithm is evaluated with the stereo video datasets from EuRoC Challenge III in terms of efficiency, accuracy and robustness. Finally, the new system is mounted and tested on a real quadrotor UAV to carry out the visual odometry and mapping task.

Funding Projects

TypeCodeAcronymLeaderTitle
FP7UnspecifiedIRSESUnspecifiedUECIMUAVS: USA and Europe Cooperation in Mini UAVs
Government of Spain2010-20751- C02-01UnspecifiedUnspecifiedMeSOANTEN projec

More information

Item ID: 42824
DC Identifier: http://oa.upm.es/42824/
OAI Identifier: oai:oa.upm.es:42824
Deposited by: Memoria Investigacion
Deposited on: 13 Jul 2016 15:40
Last Modified: 14 Jul 2016 14:59
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