Improving RGB-D Visual Odometry with Depth Learned from a Better Sensor’s Output

Kostusiak, Aleksander2023-09-252023-09-252023Kostusiak 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: 10.34658/9788366741928.67.978-83-66741-92-8http://hdl.handle.net/11652/4843https://doi.org/10.34658/9788366741928.67This 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.enDla wszystkich w zakresie dozwolonego użytkuFair use conditionvisual odometryRGB-D sensorsinpaintingdeep learningodometria wizualnaczujniki RGB-Dinpaintinggłębokie uczenie sięImproving RGB-D Visual Odometry with Depth Learned from a Better Sensor’s OutputRozdział - monografiaLicencja PŁLUT License10.34658/9788366741928.67