Autonomous vegetation identification for outdoor aerial navigation

Caterina Massidda, Heinrich Bulthoff, Paolo Stegagno

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Identification of landmarks for outdoor navigation is often performed using computationally expensive computer vision methods or via heavy and expensive multi-spectral and range sensors. Both choices are forbidden on Micro Aerial Vehicles (MAV) due to limited payload and computational power. However, an appropriate choice of the hardware sensor equipment allows the employment of mixed multi-spectral analysis and computer vision techniques to identify natural landmarks. In this work, we propose a low-cost low-weight camera array with appropriate optical filters to be exploited both as stereo camera and multi-spectral sensor. Through stereo vision and the Normalized Difference Vegetation Index (NDVI), we are able to classify the observed materials in the scene among several different classes, identify vegetation and water bodies and provide measurements of their relative bearing and distance from the robot. A handheld prototype of this camera array is tested in outdoor environment.

Original languageEnglish
Title of host publicationIEEE International Conference on Intelligent Robots and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3105-3110
Number of pages6
Volume2015-December
ISBN (Print)9781479999941
DOIs
Publication statusPublished - 2015 Dec 11
EventIEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015 - Hamburg, Germany
Duration: 2015 Sep 282015 Oct 2

Other

OtherIEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015
CountryGermany
CityHamburg
Period15/9/2815/10/2

Fingerprint

Navigation
Cameras
Antennas
Computer vision
Sensors
Bearings (structural)
Stereo vision
Optical filters
Spectrum analysis
Robots
Hardware
Costs
Water

Keywords

  • Arrays
  • Cameras
  • Lenses
  • Navigation
  • Sensors
  • Vegetation
  • Vegetation mapping

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

Cite this

Massidda, C., Bulthoff, H., & Stegagno, P. (2015). Autonomous vegetation identification for outdoor aerial navigation. In IEEE International Conference on Intelligent Robots and Systems (Vol. 2015-December, pp. 3105-3110). [7353806] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IROS.2015.7353806

Autonomous vegetation identification for outdoor aerial navigation. / Massidda, Caterina; Bulthoff, Heinrich; Stegagno, Paolo.

IEEE International Conference on Intelligent Robots and Systems. Vol. 2015-December Institute of Electrical and Electronics Engineers Inc., 2015. p. 3105-3110 7353806.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Massidda, C, Bulthoff, H & Stegagno, P 2015, Autonomous vegetation identification for outdoor aerial navigation. in IEEE International Conference on Intelligent Robots and Systems. vol. 2015-December, 7353806, Institute of Electrical and Electronics Engineers Inc., pp. 3105-3110, IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015, Hamburg, Germany, 15/9/28. https://doi.org/10.1109/IROS.2015.7353806
Massidda C, Bulthoff H, Stegagno P. Autonomous vegetation identification for outdoor aerial navigation. In IEEE International Conference on Intelligent Robots and Systems. Vol. 2015-December. Institute of Electrical and Electronics Engineers Inc. 2015. p. 3105-3110. 7353806 https://doi.org/10.1109/IROS.2015.7353806
Massidda, Caterina ; Bulthoff, Heinrich ; Stegagno, Paolo. / Autonomous vegetation identification for outdoor aerial navigation. IEEE International Conference on Intelligent Robots and Systems. Vol. 2015-December Institute of Electrical and Electronics Engineers Inc., 2015. pp. 3105-3110
@inproceedings{e2937f69e6aa47c486e27176c921823e,
title = "Autonomous vegetation identification for outdoor aerial navigation",
abstract = "Identification of landmarks for outdoor navigation is often performed using computationally expensive computer vision methods or via heavy and expensive multi-spectral and range sensors. Both choices are forbidden on Micro Aerial Vehicles (MAV) due to limited payload and computational power. However, an appropriate choice of the hardware sensor equipment allows the employment of mixed multi-spectral analysis and computer vision techniques to identify natural landmarks. In this work, we propose a low-cost low-weight camera array with appropriate optical filters to be exploited both as stereo camera and multi-spectral sensor. Through stereo vision and the Normalized Difference Vegetation Index (NDVI), we are able to classify the observed materials in the scene among several different classes, identify vegetation and water bodies and provide measurements of their relative bearing and distance from the robot. A handheld prototype of this camera array is tested in outdoor environment.",
keywords = "Arrays, Cameras, Lenses, Navigation, Sensors, Vegetation, Vegetation mapping",
author = "Caterina Massidda and Heinrich Bulthoff and Paolo Stegagno",
year = "2015",
month = "12",
day = "11",
doi = "10.1109/IROS.2015.7353806",
language = "English",
isbn = "9781479999941",
volume = "2015-December",
pages = "3105--3110",
booktitle = "IEEE International Conference on Intelligent Robots and Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - Autonomous vegetation identification for outdoor aerial navigation

AU - Massidda, Caterina

AU - Bulthoff, Heinrich

AU - Stegagno, Paolo

PY - 2015/12/11

Y1 - 2015/12/11

N2 - Identification of landmarks for outdoor navigation is often performed using computationally expensive computer vision methods or via heavy and expensive multi-spectral and range sensors. Both choices are forbidden on Micro Aerial Vehicles (MAV) due to limited payload and computational power. However, an appropriate choice of the hardware sensor equipment allows the employment of mixed multi-spectral analysis and computer vision techniques to identify natural landmarks. In this work, we propose a low-cost low-weight camera array with appropriate optical filters to be exploited both as stereo camera and multi-spectral sensor. Through stereo vision and the Normalized Difference Vegetation Index (NDVI), we are able to classify the observed materials in the scene among several different classes, identify vegetation and water bodies and provide measurements of their relative bearing and distance from the robot. A handheld prototype of this camera array is tested in outdoor environment.

AB - Identification of landmarks for outdoor navigation is often performed using computationally expensive computer vision methods or via heavy and expensive multi-spectral and range sensors. Both choices are forbidden on Micro Aerial Vehicles (MAV) due to limited payload and computational power. However, an appropriate choice of the hardware sensor equipment allows the employment of mixed multi-spectral analysis and computer vision techniques to identify natural landmarks. In this work, we propose a low-cost low-weight camera array with appropriate optical filters to be exploited both as stereo camera and multi-spectral sensor. Through stereo vision and the Normalized Difference Vegetation Index (NDVI), we are able to classify the observed materials in the scene among several different classes, identify vegetation and water bodies and provide measurements of their relative bearing and distance from the robot. A handheld prototype of this camera array is tested in outdoor environment.

KW - Arrays

KW - Cameras

KW - Lenses

KW - Navigation

KW - Sensors

KW - Vegetation

KW - Vegetation mapping

UR - http://www.scopus.com/inward/record.url?scp=84958179168&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84958179168&partnerID=8YFLogxK

U2 - 10.1109/IROS.2015.7353806

DO - 10.1109/IROS.2015.7353806

M3 - Conference contribution

AN - SCOPUS:84958179168

SN - 9781479999941

VL - 2015-December

SP - 3105

EP - 3110

BT - IEEE International Conference on Intelligent Robots and Systems

PB - Institute of Electrical and Electronics Engineers Inc.

ER -