Simultaneous Localization And Mapping with depth Prediction using Capsule Networks for UAVs
This addresses UAV navigation challenges, but appears incremental as it combines existing methods like CapsNet and EKF for SLAM.
The paper tackles SLAM for UAVs by proposing a novel system using Capsule Networks for depth prediction to overcome CNN drawbacks, with results evaluated on a benchmark dataset to show accuracy.
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.