Visual Localization Using Capsule Networks

Visual localization is the task of camera pose estimation, and is crucial for many technologies which involve localization such as mobile robots and augmented reality. Several convolutional neural network models have been proposed for the task against the more accurate geometry based computer vision techniques. However, they have several shortcomings and to our knowledge, this was the first effort that explored the use of an alternative architecture based on capsule-networks for the task. We achieved better results with capsules than with baseline-CNN PoseNet on small NORB dataset, modified for the task of camera pose estimation. Feature visualizations for both the networks produced more insights on their performance and behaviour. We found that there is a scope for improvement and hence propose few directions for future efforts.