Simultaneous Localization And Mapping with depth Prediction using Capsule Networks for UAVs

Abstract-- 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. I. INTRODUCTION Simultaneous localization and mapping (SLAM) has a significant role to play in helping autonomous robots to navigate their way around an uncertain environment and this has many widespread implications in various industries. For instance, drones can be programmed to find their way in a logistics warehouse without prior knowledge of the space, in order to retrieve information of a particular package.