Evaluation and Optimization of Feature Detectors Towards Off-Road Visual Odometry

dc.contributor.authorTepandi, Tarvi
dc.contributor.otherTartu Ülikool. Loodus- ja täppisteaduste valdkondet
dc.contributor.otherTartu Ülikool. Tehnoloogiainstituutet
dc.date.accessioned2023-10-09T13:58:51Z
dc.date.available2023-10-09T13:58:51Z
dc.date.issued2023
dc.description.abstractFeature detectors are used in many computer vision applications, including Visual Odometry (VO). However, their utilization in off-road VO remains a topic of great interest. In this body of work, software tools are developed in order to evaluate and parameter-optimize feature detectors towards real-time off-road VO. Using the tools developed, various classical as well as state-of-the-art machine learning-based feature detectors are evaluated and optimized, including their usage in a pre-existing VO implementation and analyzing the output trajectory. The analysis and results presented show that although the quality of feature detectors have an impact, optimizing them alone cannot overcome the inherent drawbacks of monocular VO approaches. Based on the analysis, recommendations of potential feature detectors that can support real-time off-road VO are made and optimized parameters of selected feature detectors are provided. Key areas of improvement for future research in the field are also identified.et
dc.identifier.urihttps://hdl.handle.net/10062/93442
dc.language.isoenget
dc.publisherTartu Ülikoolet
dc.rightsembargoedAccesset
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectComputer Vision, Feature Detection, Visual Odometryet
dc.subject.othermagistritöödet
dc.titleEvaluation and Optimization of Feature Detectors Towards Off-Road Visual Odometryet
dc.typeThesiset

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