A Recurrent Deep Network for Estimating the Pose of Real Indoor Images from Synthetic Image Sequences
Debaditya Acharya, Sesa Singha Roy, Kourosh Khoshelham, Stephan Winter
SENSORS | MDPI | Published : 2020
Recently, deep convolutional neural networks (CNN) have become popular for indoor visual localisation, where the networks learn to regress the camera pose from images directly. However, these approaches perform a 3D image-based reconstruction of the indoor spaces beforehand to determine camera poses, which is a challenge for large indoor spaces. Synthetic images derived from 3D indoor models have been used to eliminate the requirement of 3D reconstruction. A limitation of the approach is the low accuracy that occurs as a result of estimating the pose of each image frame independently. In this article, a visual localisation approach is proposed that exploits the spatio-temporal information fr..View full abstract
Awarded by LIEF Grant
This research is supported by a Research Engagement Grant from the Melbourne School of Engineering and a Melbourne Research Scholarship. This research was undertaken using the LIEF HPC-GPGPU Facility hosted at the University of Melbourne. This Facility was established with the assistance of LIEF Grant LE170100200.