國立虎尾科技大學 |

Sensor Fusion and Deep Learning for Indoor Agent Localization.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Sensor Fusion and Deep Learning for Indoor Agent Localization./
Author:	Lauzon, Jacob F.
Description:	1 online resource (123 pages)
Notes:	Source: Masters Abstracts International, Volume: 56-06.
Contained By:	Masters Abstracts International56-06(E).
Subject:	Computer engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9780355158724

Sensor Fusion and Deep Learning for Indoor Agent Localization.
Lauzon, Jacob F.

Sensor Fusion and Deep Learning for Indoor Agent Localization. - 1 online resource (123 pages)

Source: Masters Abstracts International, Volume: 56-06.

Thesis (M.S.)--Rochester Institute of Technology, 2017.

Includes bibliographical references

Autonomous, self-navigating agents have been rising in popularity due to a push for a more technologically aided future. From cars to vacuum cleaners, the applications of self-navigating agents are vast and span many different fields and aspects of life. As the demand for these autonomous robotic agents has been increasing, so has the demand for innovative features, robust behavior, and lower cost hardware. One particular area with a constant demand for improvement is localization, or an agent's ability to determine where it is located within its environment. Whether the agent's environment is primarily indoor or outdoor, dense or sparse, static or dynamic, an agent must be able to have knowledge of its location. Many different techniques exist today for localization, each having its strengths and weaknesses. Despite the abundance of different techniques, there is still room for improvement. This research presents a novel indoor localization algorithm that fuses data from multiple sensors for a relatively low cost. Inspired by recent innovations in deep learning and particle filters, a fast, robust, and accurate autonomous localization system has been created. Results demonstrate that the proposed system is both real-time and robust against changing conditions within the environment.

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

Mode of access: World Wide Web

ISBN: 9780355158724Subjects--Topical Terms:

569006
Computer engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Sensor Fusion and Deep Learning for Indoor Agent Localization.
LDR:02500ntm a2200337Ki 4500 001 920649
005 20181203094031.5
006 m o u
007 cr mn||||a|a||
008 190606s2017 xx obm 000 0 eng d
020 $a 9780355158724
035 $a (MiAaPQ)AAI10605831
035 $a (MiAaPQ)rit:12697
035 $a AAI10605831
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Lauzon, Jacob F. $3 1195510
245 1 0 $a Sensor Fusion and Deep Learning for Indoor Agent Localization.
264 0 $c 2017
300 $a 1 online resource (123 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 56-06.
500 $a Adviser: Raymond Ptucha.
502 $a Thesis (M.S.)--Rochester Institute of Technology, 2017.
504 $a Includes bibliographical references
520 $a Autonomous, self-navigating agents have been rising in popularity due to a push for a more technologically aided future. From cars to vacuum cleaners, the applications of self-navigating agents are vast and span many different fields and aspects of life. As the demand for these autonomous robotic agents has been increasing, so has the demand for innovative features, robust behavior, and lower cost hardware. One particular area with a constant demand for improvement is localization, or an agent's ability to determine where it is located within its environment. Whether the agent's environment is primarily indoor or outdoor, dense or sparse, static or dynamic, an agent must be able to have knowledge of its location. Many different techniques exist today for localization, each having its strengths and weaknesses. Despite the abundance of different techniques, there is still room for improvement. This research presents a novel indoor localization algorithm that fuses data from multiple sensors for a relatively low cost. Inspired by recent innovations in deep learning and particle filters, a fast, robust, and accurate autonomous localization system has been created. Results demonstrate that the proposed system is both real-time and robust against changing conditions within the environment.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer engineering. $3 569006
650 4 $a Robotics. $3 561941
655 7 $a Electronic books. $2 local $3 554714
690 $a 0464
690 $a 0771
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Rochester Institute of Technology. $b Computer Engineering. $3 1184443
773 0 $t Masters Abstracts International $g 56-06(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10605831 $z click for full text (PQDT)