Optimization- and Learning-Based Approaches to Visual SLAM and Relocalization

von Stumberg, Lukas Michael

Lukas Michael von Stumberg

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Original title:: Optimization- and Learning-Based Approaches to Visual SLAM and Relocalization
Translated title:: Optimierungs- und lernbasierte Verfahren für visuelles SLAM und Relokalisierung
Author:: von Stumberg, Lukas Michael
Year:: 2023
Document type:: Dissertation
Faculty/School:: TUM School of Computation, Information and Technology
Advisor:: Cremers, Daniel (Prof. Dr.)
Referee:: Cremers, Daniel (Prof. Dr.); Scaramuzza, Davide (Prof. Dr.); Fallon, Maurice (Prof., Ph.D.)
Language:: en
Subject group:: DAT Datenverarbeitung, Informatik
TUM classification:: DAT 760; DAT 770
Abstract:: Visual SLAM and Relocalization are essential for a number of applications like robotics, autonomous driving and augmented reality. The first part of this thesis introduces new techniques for optimization-based visual-inertial odometry, including an optimization technique, IMU initializers and marginalization variants. Afterwards we improve visual odometry and relocalization by employing neural networks which estimate depths, uncertainty, relative poses, and deep features.
Translated abstract:: Visuelles SLAM und Relokalisierung sind essentiell für Anwendungen wie Robotik, autonomes Fahren und Augmented Reality. Der erste Teil dieser Dissertation stellt neue Techniken für optimierungs-basierte visuell-inertiale Odometrie vor, darunter ein Optimierungsverfahren, IMU-Initialisierer und Marginalierungs-Verfahren. Anschließend verbessern wir visuelle Odometrie und Relokalisierung, indem wir neuronale Netze integrieren, die Tiefenwerte, Unsicherheit, relative Posen und Features berechnen.
WWW:: https://mediatum.ub.tum.de/?id=1685479
Notes:: In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of TUM's products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink. «
In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of TUM's products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_standards/publications/rights/rights_link.html to learn how to obtain a Lic... »
Date of submission:: 24.08.2022
Oral examination:: 21.08.2023
File size:: 44418311 bytes
Pages:: 253
Urn (citeable URL):: https://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:91-diss-20230821-1685479-1-5
Last change:: 26.10.2023
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