Please use this identifier to cite or link to this item:
Title: Localization of an unmanned aerial vehicle in an indoor environment
Authors: Chen, Xing Yu
Keywords: DRNTU::Engineering
Issue Date: 2016
Abstract: Unmanned aerial vehicle (UAV) is also called a drone. In contradiction to other types of aircrafts, it does not have a pilot on board however it can be controlled by several other modes instead, for instance, Remote Control mode is flown by human on the ground station or Autonomously flown by preprogrammed plan. For both of these control types, knowing the location of the aircraft is so important that it offers operator the control flexibility and information so as to make any corrections of the control command in real time. Meanwhile, constantly feedback the locations or the waypoints to the control units laid the foundation of Autonomous piloting activities and played an even critical role in Autonomous mode since the decision making is taken over by computer processers instead of human senses. In order to achieve fully autonomous, obstacle avoiding and self-localization needed to be constantly improve on as well. The most common UAV self-localization method is through GPS communication, it works perfect in open air condition where the communication signal is good. However it losses its capability significantly when GPS data is not available, indoor environment could be one of the scenarios which it faces such challenges. In this report, we will take a look at another auxiliary approach based on computer vision to serve as an alternative. It aims to computer the correlation of different picture in real time hence to get the amount of transform carried out from current position to previous waypoint and determine the current position if the starting point is known. The algorithm of the method will be discussed and details computer vision technic will be studies as well.
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
U1321264A Chen Xing yuFYP_Report.pdf
  Restricted Access
main report of UAV localization using SURF1.48 MBAdobe PDFView/Open

Page view(s)

Updated on Jun 19, 2021


Updated on Jun 19, 2021

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.