Open AccessDepthTiling: A novel way to increase visual SLAM performance in featureless environments
Author(s) -
Altuntaş N.,
Amasyalı M.F.
Publication year - 2019
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2019.1384
Subject(s) - visual odometry , artificial intelligence , computer vision , rgb color model , computer science , feature (linguistics) , odometry , simultaneous localization and mapping , feature extraction , tracking (education) , image (mathematics) , robot , mobile robot , psychology , pedagogy , linguistics , philosophy
The common problem of visual simultaneous localisation and mapping systems is to suffer from featureless environments. It is possible for all environments to have such featureless situations; even though most of the mapped areas contain sufficient textures. This Letter brings a new approach using not only RGB values of the objects but also their positions in the map for feature extraction in order to decrease odometry loss in such situations. DepthTiling recolours RGB image using associated depth data. The experiments give promising results to increase the capability of visual odometry tracking. This study shows that it is possible to increase number of features using related depth data when RGB images are insufficient.