  Localization is an indispensable component of a robot's autonomy stack that
enables it to determine where it is in the environment, essentially making it a
precursor for any action execution or planning. Although convolutional neural
networks have shown promising results for visual localization, they are still
grossly outperformed by state-of-the-art local feature-based techniques. In
this work, we propose VLocNet, a new convolutional neural network architecture
for 6-DoF global pose regression and odometry estimation from consecutive
monocular images. Our multitask model incorporates hard parameter sharing, thus
being compact and enabling real-time inference, in addition to being end-to-end
trainable. We propose a novel loss function that utilizes auxiliary learning to
leverage relative pose information during training, thereby constraining the
search space to obtain consistent pose estimates. We evaluate our proposed
VLocNet on indoor as well as outdoor datasets and show that even our single
task model exceeds the performance of state-of-the-art deep architectures for
global localization, while achieving competitive performance for visual
odometry estimation. Furthermore, we present extensive experimental evaluations
utilizing our proposed Geometric Consistency Loss that show the effectiveness
of multitask learning and demonstrate that our model is the first deep learning
technique to be on par with, and in some cases outperforms state-of-the-art
SIFT-based approaches.
