  Convolutional neural networks (CNNs) and transfer learning have recently been
used for 6 degrees of freedom (6-DoF) camera pose estimation. While they do not
reach the same accuracy as visual SLAM-based approaches and are restricted to a
specific environment, they excel in robustness and can be applied even to a
single image. In this paper, we study PoseNet [1] and investigate modifications
based on datasets' characteristics to improve the accuracy of the pose
estimates. In particular, we emphasize the importance of field-of-view over
image resolution; we present a data augmentation scheme to reduce overfitting;
we study the effect of Long-Short-Term-Memory (LSTM) cells. Lastly, we combine
these modifications and improve PoseNet's performance for monocular CNN based
camera pose regression.
