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.
@cite_0: The problem of development a sustainable efficient and accurate augmented reality system possessing the interactivity property and basically implementing markerless approach for supplementing the real scene is addressed in this paper. Custom implementation is based on applying of feature-based SLAM algorithm, which is resistant to change light conditions, able to track both textured and non-textured objects in real-time mode achieved by introducing a once-performed offline system teaching step. The proposed solution is universal because of the system’s configurability in accordance with the desirable result.
Further, a few more algorithms which are also good in detecting keypoints like FAST Corner detector presented by Roston and Drummond in 2006 @cite_0 and Laplacian of Gaussian (LoG) blob detector @cite_0 which finds blobs in image instead of corners identified using Laplacian on the smoothed image.