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Deep Learning-Based Monocular SLAM.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Deep Learning-Based Monocular SLAM./
Author:	Rupaakula, Krishna Sandeep.
Description:	1 online resource (81 pages)
Notes:	Source: Masters Abstracts International, Volume: 84-11.
Contained By:	Masters Abstracts International84-11.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9798379524906

Deep Learning-Based Monocular SLAM.
Rupaakula, Krishna Sandeep.

Deep Learning-Based Monocular SLAM. - 1 online resource (81 pages)

Source: Masters Abstracts International, Volume: 84-11.

Thesis (M.S.)--Arizona State University, 2023.

Includes bibliographical references

SLAM (Simultaneous Localization and Mapping) is a problem that has existed for a long time in robotics and autonomous navigation. The objective of SLAM is for a robot to simultaneously figure out its position in space and map its environment. SLAM is especially useful and mandatory for robots that want to navigate autonomously. The description might make it seem like a chicken and egg problem, but numerous methods have been proposed to tackle SLAM. Before the rise in the popularity of deep learning and AI (Artificial Intelligence), most existing algorithms involved traditional hard-coded algorithms that would receive and process sensor information and convert it into some solvable sensor-agnostic problem. The challenge for these sorts of methods is having to tackle dynamic environments. The more variety in the environment, the poorer the results. Also due to the increase in computational power and the capability of deep learning-based image processing, visual SLAM has become extremely viable and maybe even preferable to traditional SLAM algorithms. In this research, a deep learning-based solution to the SLAM problem is proposed, specifically monocular visual SLAM which is solving the problem of SLAM purely with a singular camera as the input, and the model is tested on the KITTI (Karlsruhe Institute of Technology & Toyota Technological Institute) odometry dataset.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379524906Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Deep learningIndex Terms--Genre/Form:

554714
Electronic books.

Deep Learning-Based Monocular SLAM.
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500 $a Advisor: Bansal, Ajay.
502 $a Thesis (M.S.)--Arizona State University, 2023.
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520 $a SLAM (Simultaneous Localization and Mapping) is a problem that has existed for a long time in robotics and autonomous navigation. The objective of SLAM is for a robot to simultaneously figure out its position in space and map its environment. SLAM is especially useful and mandatory for robots that want to navigate autonomously. The description might make it seem like a chicken and egg problem, but numerous methods have been proposed to tackle SLAM. Before the rise in the popularity of deep learning and AI (Artificial Intelligence), most existing algorithms involved traditional hard-coded algorithms that would receive and process sensor information and convert it into some solvable sensor-agnostic problem. The challenge for these sorts of methods is having to tackle dynamic environments. The more variety in the environment, the poorer the results. Also due to the increase in computational power and the capability of deep learning-based image processing, visual SLAM has become extremely viable and maybe even preferable to traditional SLAM algorithms. In this research, a deep learning-based solution to the SLAM problem is proposed, specifically monocular visual SLAM which is solving the problem of SLAM purely with a singular camera as the input, and the model is tested on the KITTI (Karlsruhe Institute of Technology & Toyota Technological Institute) odometry dataset.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
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653 $a Deep learning
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653 $a Autonomous navigation
653 $a Dynamic environments
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