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High-Speed Vision-Based Autonomous I...

Garcia, Adriano.

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High-Speed Vision-Based Autonomous Indoor Quadrotor Navigation.

Record Type:	Electronic resources : Monograph/item
Title/Author:	High-Speed Vision-Based Autonomous Indoor Quadrotor Navigation./
Author:	Garcia, Adriano.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	182 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-05, Section: B.
Contained By:	Dissertations Abstracts International82-05B.
Subject:	Robotics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28030911
ISBN:	9798678160843

High-Speed Vision-Based Autonomous Indoor Quadrotor Navigation.
Garcia, Adriano.

High-Speed Vision-Based Autonomous Indoor Quadrotor Navigation. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 182 p.

Source: Dissertations Abstracts International, Volume: 82-05, Section: B.

Thesis (Ph.D.)--State University of New York at Binghamton, 2020.

This item must not be sold to any third party vendors.

Autonomous vision-based navigation is one of the most rapidly expanding research fields in the industrial and academic worlds. A particularly new and potentially impactful research area involves the use of camera-enabled vision systems that permit quadrotor drones to navigate autonomously in structured indoor environments. Such autonomous navigation systems permit flights that are difficult to realize with a human controlling the drone based on visual cues in situations where the human controller cannot intervene in a timely or reliable fashion to deal with rapidly evolving flight situations. Autonomous techniques for navigating the drones in a reliable and consistent manner open up applications such as surveillance, situation assessments, mapping/exploration of unknown buildings and, in general, dealing with many emergencies. This dissertation develops a vision-based system technique for enabling the autonomous navigation of drones in building hallways based on the off-board processing of a monocular video stream transmitted from the drone's forward-facing camera.Indoor autonomous navigation using quadrotors is an especially challenging endeavor given the limitations and constraints due to the environment, the hardware, and the software currently available. As such, the work presented in this dissertation takes a holistic approach where the challenges and limitations of quadrotor aircraft are addressed via the use of proactive vision-based control algorithms that perceive the environment in order to understand it and develop a best course of action that permits uninterrupted autonomous flights at relatively high speeds. New methods to enable high-speed real-time flights are presented, where images acquired from a single front-facing camera onboard the quadrotor drone are used to extract environmental features that permit the drone to localize itself within the environment.The approach presented is purely vision-based where image processing, machine learning, and deep learning methods are leveraged to provide the external sensory mechanisms by which the environment is perceived and understood. The use of a single monocular front-facing camera affords several key benefits: no additional sensors or hardware are needed, no additional modification to the quadrotor platform is required, and precious onboard resources are saved due to reductions in payload and power consumption. In addition, since hardware requirements are low, our control software can easily be ported to other platforms.The autonomous system is validated through a wide variety of flight tests that demonstrate real-world consistent and reliable vision-based navigation in environments where few or no previous autonomous flights were feasible.

ISBN: 9798678160843Subjects--Topical Terms:

519753
Robotics.
Subjects--Index Terms:

Autonomous Navigation

High-Speed Vision-Based Autonomous Indoor Quadrotor Navigation.
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520 $a Autonomous vision-based navigation is one of the most rapidly expanding research fields in the industrial and academic worlds. A particularly new and potentially impactful research area involves the use of camera-enabled vision systems that permit quadrotor drones to navigate autonomously in structured indoor environments. Such autonomous navigation systems permit flights that are difficult to realize with a human controlling the drone based on visual cues in situations where the human controller cannot intervene in a timely or reliable fashion to deal with rapidly evolving flight situations. Autonomous techniques for navigating the drones in a reliable and consistent manner open up applications such as surveillance, situation assessments, mapping/exploration of unknown buildings and, in general, dealing with many emergencies. This dissertation develops a vision-based system technique for enabling the autonomous navigation of drones in building hallways based on the off-board processing of a monocular video stream transmitted from the drone's forward-facing camera.Indoor autonomous navigation using quadrotors is an especially challenging endeavor given the limitations and constraints due to the environment, the hardware, and the software currently available. As such, the work presented in this dissertation takes a holistic approach where the challenges and limitations of quadrotor aircraft are addressed via the use of proactive vision-based control algorithms that perceive the environment in order to understand it and develop a best course of action that permits uninterrupted autonomous flights at relatively high speeds. New methods to enable high-speed real-time flights are presented, where images acquired from a single front-facing camera onboard the quadrotor drone are used to extract environmental features that permit the drone to localize itself within the environment.The approach presented is purely vision-based where image processing, machine learning, and deep learning methods are leveraged to provide the external sensory mechanisms by which the environment is perceived and understood. The use of a single monocular front-facing camera affords several key benefits: no additional sensors or hardware are needed, no additional modification to the quadrotor platform is required, and precious onboard resources are saved due to reductions in payload and power consumption. In addition, since hardware requirements are low, our control software can easily be ported to other platforms.The autonomous system is validated through a wide variety of flight tests that demonstrate real-world consistent and reliable vision-based navigation in environments where few or no previous autonomous flights were feasible.
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653 $a Autonomous Vehicles
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