  In this paper, we propose an novel implementation of a simultaneous
localization and mapping (SLAM) system based on a monocular camera from an
unmanned aerial vehicle (UAV) using Depth prediction performed with Capsule
Networks (CapsNet), which possess improvements over the drawbacks of the more
widely-used Convolutional Neural Networks (CNN). An Extended Kalman Filter will
assist in estimating the position of the UAV so that we are able to update the
belief for the environment. Results will be evaluated on a benchmark dataset to
portray the accuracy of our intended approach.
