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Autonomous Indoor Pathfinding Using Neural Networks in Complex Scenes.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Autonomous Indoor Pathfinding Using Neural Networks in Complex Scenes./
Author:	Vasudevan, Vignesh.
Description:	1 online resource (42 pages)
Notes:	Source: Masters Abstracts International, Volume: 57-06.
Contained By:	Masters Abstracts International57-06(E).
Subject:	Electrical engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9780438124813

Autonomous Indoor Pathfinding Using Neural Networks in Complex Scenes.
Vasudevan, Vignesh.

Autonomous Indoor Pathfinding Using Neural Networks in Complex Scenes. - 1 online resource (42 pages)

Source: Masters Abstracts International, Volume: 57-06.

Thesis (M.S.)--Illinois Institute of Technology, 2018.

Includes bibliographical references

Navigation to a specific destination indoors can be a challenge due to different reasons such as visual impairment, unknown environments, etc. There has been much work done to solve this issue such as indoor positioning systems, navigation using sensors and even using a robotic guide. In this thesis, a novel and straightforward method of path planning (including object avoidance) is presented as a way of navigating to a desired location within a complex environment. The system proposed uses the combination of depth information from an RGB-D camera and the object information from a Neural Network based object identification technique, to efficiently calculate and plan a path in real-time, to a pre-specified destination. Persons to be helped are identified using object detection, and the most practical path to the desired destination is calculated. The path information would be sent to the handheld device of the person being helped in the suitable form of interface, such as visual, audio, etc. The surveillance type nature of the system enables it to help multiple persons in the same area. The model was tested in a controlled environment with one individual person being guided to nearby specified locations. The testing showed promising results, validating the performance of the system.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780438124813Subjects--Topical Terms:

596380
Electrical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Autonomous Indoor Pathfinding Using Neural Networks in Complex Scenes.
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245 1 0 $a Autonomous Indoor Pathfinding Using Neural Networks in Complex Scenes.
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300 $a 1 online resource (42 pages)
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500 $a Source: Masters Abstracts International, Volume: 57-06.
500 $a Adviser: Jafar Saniie.
502 $a Thesis (M.S.)--Illinois Institute of Technology, 2018.
504 $a Includes bibliographical references
520 $a Navigation to a specific destination indoors can be a challenge due to different reasons such as visual impairment, unknown environments, etc. There has been much work done to solve this issue such as indoor positioning systems, navigation using sensors and even using a robotic guide. In this thesis, a novel and straightforward method of path planning (including object avoidance) is presented as a way of navigating to a desired location within a complex environment. The system proposed uses the combination of depth information from an RGB-D camera and the object information from a Neural Network based object identification technique, to efficiently calculate and plan a path in real-time, to a pre-specified destination. Persons to be helped are identified using object detection, and the most practical path to the desired destination is calculated. The path information would be sent to the handheld device of the person being helped in the suitable form of interface, such as visual, audio, etc. The surveillance type nature of the system enables it to help multiple persons in the same area. The model was tested in a controlled environment with one individual person being guided to nearby specified locations. The testing showed promising results, validating the performance of the system.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Electrical engineering. $3 596380
655 7 $a Electronic books. $2 local $3 554714
690 $a 0544
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Illinois Institute of Technology. $b Electrical and Computer Engineering. $3 1179314
773 0 $t Masters Abstracts International $g 57-06(E).
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