Object detection technique for small unmanned aerial vehicle

Obstacle detection and avoidance is desirable for UAVs especially lightweight micro aerial vehicles and is challenging problem since it has payload constraints, therefore only limited sensor can be attached the vehicle. Usually the sensors incorporated in the system is either type vision based (mono...

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Main Authors:	Ramli, M. Faiz, Legowo, Ari, Shamsudin, Syariful Syafiq
Format:	Conference or Workshop Item
Language:	English English English English
Published:	IOP 2017
Subjects:	TL500 Aeronautics
Online Access:	http://irep.iium.edu.my/58901/ http://irep.iium.edu.my/58901/ http://irep.iium.edu.my/58901/1/ICOM_2017_Ari%20Legowo.pdf http://irep.iium.edu.my/58901/7/58901_Object%20Detection%20Technique_tentative.pdf http://irep.iium.edu.my/58901/18/58901_Object%20detection%20technique.pdf http://irep.iium.edu.my/58901/24/58901%20Object%20detection%20technique%20for%20small%20unmanned%20aerial%20vehicle%20SCOPUS.pdf

id	iium-58901
recordtype	eprints
spelling	iium-589012018-03-23T02:17:39Z http://irep.iium.edu.my/58901/ Object detection technique for small unmanned aerial vehicle Ramli, M. Faiz Legowo, Ari Shamsudin, Syariful Syafiq TL500 Aeronautics Obstacle detection and avoidance is desirable for UAVs especially lightweight micro aerial vehicles and is challenging problem since it has payload constraints, therefore only limited sensor can be attached the vehicle. Usually the sensors incorporated in the system is either type vision based (monocular or stereo camera) or Laser based. However, each of the sensor has its own advantage and disadvantage, thus we built the obstacle detection and avoidance system based multi sensor (monocular sensor and LIDAR) integration. On top of that, we also combine SURF algorithm with Harris corner detector to determine the approximate size of the obstacles. In the initial experiment conducted, we successfully detect and determine the size of the obstacles with 3 different obstacles. The differences of length between real obstacles and our algorithm are considered acceptable which is about -0.4 to 3.6. IOP 2017-08-08 Conference or Workshop Item NonPeerReviewed application/pdf en http://irep.iium.edu.my/58901/1/ICOM_2017_Ari%20Legowo.pdf application/pdf en http://irep.iium.edu.my/58901/7/58901_Object%20Detection%20Technique_tentative.pdf application/pdf en http://irep.iium.edu.my/58901/18/58901_Object%20detection%20technique.pdf application/pdf en http://irep.iium.edu.my/58901/24/58901%20Object%20detection%20technique%20for%20small%20unmanned%20aerial%20vehicle%20SCOPUS.pdf Ramli, M. Faiz and Legowo, Ari and Shamsudin, Syariful Syafiq (2017) Object detection technique for small unmanned aerial vehicle. In: 6th International Conference on Mechatronics (ICOM'17), 8th-9th August 2017, Kuala Lumpur. http://www.iium.edu.my/icom/
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English English English
topic	TL500 Aeronautics
spellingShingle	TL500 Aeronautics Ramli, M. Faiz Legowo, Ari Shamsudin, Syariful Syafiq Object detection technique for small unmanned aerial vehicle
description	Obstacle detection and avoidance is desirable for UAVs especially lightweight micro aerial vehicles and is challenging problem since it has payload constraints, therefore only limited sensor can be attached the vehicle. Usually the sensors incorporated in the system is either type vision based (monocular or stereo camera) or Laser based. However, each of the sensor has its own advantage and disadvantage, thus we built the obstacle detection and avoidance system based multi sensor (monocular sensor and LIDAR) integration. On top of that, we also combine SURF algorithm with Harris corner detector to determine the approximate size of the obstacles. In the initial experiment conducted, we successfully detect and determine the size of the obstacles with 3 different obstacles. The differences of length between real obstacles and our algorithm are considered acceptable which is about -0.4 to 3.6.
format	Conference or Workshop Item
author	Ramli, M. Faiz Legowo, Ari Shamsudin, Syariful Syafiq
author_facet	Ramli, M. Faiz Legowo, Ari Shamsudin, Syariful Syafiq
author_sort	Ramli, M. Faiz
title	Object detection technique for small unmanned aerial vehicle
title_short	Object detection technique for small unmanned aerial vehicle
title_full	Object detection technique for small unmanned aerial vehicle
title_fullStr	Object detection technique for small unmanned aerial vehicle
title_full_unstemmed	Object detection technique for small unmanned aerial vehicle
title_sort	object detection technique for small unmanned aerial vehicle
publisher	IOP
publishDate	2017
url	http://irep.iium.edu.my/58901/ http://irep.iium.edu.my/58901/ http://irep.iium.edu.my/58901/1/ICOM_2017_Ari%20Legowo.pdf http://irep.iium.edu.my/58901/7/58901_Object%20Detection%20Technique_tentative.pdf http://irep.iium.edu.my/58901/18/58901_Object%20detection%20technique.pdf http://irep.iium.edu.my/58901/24/58901%20Object%20detection%20technique%20for%20small%20unmanned%20aerial%20vehicle%20SCOPUS.pdf
first_indexed	2023-09-18T21:23:21Z
last_indexed	2023-09-18T21:23:21Z
_version_	1777412029159571456

Object detection technique for small unmanned aerial vehicle

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