GPS-SLAM: An augmentation of the ORB-SLAM algorithm

This work presents Global Positioning System-Simultaneous Localization and Mapping (GPS-SLAM), an augmented version of Oriented FAST (Features from accelerated segment test) and Rotated BRIEF (Binary Robust Independent Elementary Features) feature detector (ORB)-SLAM using GPS and inertial data to make the algorithm capable of dealing with low frame rate datasets. In general, SLAM systems are successful in case of datasets with a high frame rate. This work was motivated by a scarce dataset where ORB-SLAM often loses track because of the lack of continuity. The main work includes the determination of the next frame’s pose based on the GPS and inertial data. The results show that this additional information makes the algorithm more robust. As many large, outdoor unmanned aerial vehicle (UAV) flights save the GPS and inertial measurement unit (IMU) data of the capturing of images, this program gives an option to use the SLAM algorithm successfully even if the dataset has a low frame rate

Pre-Tactical Prediction of Atfm Delay for Individual Flights

The day prior operations, the operation plan is drawn generating a first set of flight plans with the objective of identifying potential network issues and preparing pre-tactical preventing measures. During the day of operation flight plans will be updated and pre-tactical actions implemented if needed by the duty manager, in order to minimise the propagation of disruption in the network. This paper focuses on the estimation of ATFM delay for individual flights during the pre-tactical phase. The main objective is to anticipate which flights might be affected by ATFM regulations and the amount of delay will be assigned to them