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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.

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Showing 1 to 2 of 2 (0.208 seconds)

Spelling suggestions: "subject:"schwerewellenparametrisierung"" "subject:"konvektionsparametrisierung""

1

Towards Dense Visual SLAM

Pietzsch, Tobias 05 December 2011 (has links) (PDF)
Visual Simultaneous Localisation and Mapping (SLAM) is concerned with simultaneously estimating the pose of a camera and a map of the environment from a sequence of images. Traditionally, sparse maps comprising isolated point features have been employed, which facilitate robust localisation but are not well suited to advanced applications. In this thesis, we present map representations that allow a more dense description of the environment. In one approach, planar features are used to represent textured planar surfaces in the scene. This model is applied within a visual SLAM framework based on the Extended Kalman Filter. We presents solutions to several challenges which arise from this approach.
Simultane Lokalisierung und Kartierung
SLAM
Visuelles SLAM
Erweiterte Realität
Inverse-Tiefe-Parametrisierung
Bildverarbeitung
Dreidimensionales Maschinelles Sehen
Stereo-Sehen
Flächen-Modelle
Simultaneous Localization And Mapping
Visual SLAM
Stereo Vision
Augmented Reality
Extended Kalman Filter
Inverse Depth
Higher-Level Features
Planar Features
Dense Mapping
Dense Maps
Direct Measurement Method
Visual SLAM Evaluation
ddc:004
rvk:ST 308
SLAM-Verfahren
Erweiterte Realität <Informatik>
Bildfolgenverarbeitung
Bildverarbeitung
Lokalisierung <Robotik>
Dreidimensionales maschinelles Sehen
2

Towards Dense Visual SLAM

Pietzsch, Tobias 07 June 2011 (has links)
Visual Simultaneous Localisation and Mapping (SLAM) is concerned with simultaneously estimating the pose of a camera and a map of the environment from a sequence of images. Traditionally, sparse maps comprising isolated point features have been employed, which facilitate robust localisation but are not well suited to advanced applications. In this thesis, we present map representations that allow a more dense description of the environment. In one approach, planar features are used to represent textured planar surfaces in the scene. This model is applied within a visual SLAM framework based on the Extended Kalman Filter. We presents solutions to several challenges which arise from this approach.
info:eu-repo/classification/ddc/004
ddc:004

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