A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf
.
Modelling uncertainty in deep learning for camera relocalization
2016
2016 IEEE International Conference on Robotics and Automation (ICRA)
We present a robust and real-time monocular six degree of freedom visual relocalization system. We use a Bayesian convolutional neural network to regress the 6-DOF camera pose from a single RGB image. It is trained in an end-to-end manner with no need of additional engineering or graph optimisation. The algorithm can operate indoors and outdoors in real time, taking under 6ms to compute. It obtains approximately 2m and 6 • accuracy for very large scale outdoor scenes and 0.5m and 10 • accuracy
doi:10.1109/icra.2016.7487679
dblp:conf/icra/KendallC16
fatcat:7zfus43jt5efnpebfhiohmi4aq