Improving RGB-D Visual Odometry with Depth Learned from a Better Sensor’s Output
Data
2023
Autorzy
Tytuł czasopisma
ISSN czasopisma
Tytuł tomu
Wydawca
Wydawnictwo Politechniki Łódzkiej
Lodz University of Technology Press
Lodz University of Technology Press
Abstrakt
This paper compares the results obtained from an indoor Visual
Odometry (VO) system with RGB-D images provided by a Kinect v1 camera
against those achieved by a VO with enhanced depth channel. For this
purpose, we have used two classic image inpainting methods and a deeplearning
approach for scene depth estimation employing Kinect v2 depth
maps as reference data. The ability to enhance lower-quality data is crucial
to reduce the cost of VO applications because higher-quality information
can be infused through deep learning in systems using budget sensors.
Opis
Słowa kluczowe
visual odometry, RGB-D sensors, inpainting, deep learning, odometria wizualna, czujniki RGB-D, inpainting, głębokie uczenie się
Cytowanie
Kostusiak A., Improving RGB-D Visual Odometry with Depth Learned from a Better Sensor’s Output. W: Progress in Polish Artificial Intelligence Research 4, Wojciechowski A. (Ed.), Lipiński P. (Ed.)., Seria: Monografie Politechniki Łódzkiej Nr. 2437, Wydawnictwo Politechniki Łódzkiej, Łódź 2023, s. 429-434, ISBN 978-83-66741-92-8, doi: 10.34658/9788366741928.67.