A Parallel Multilevel Data Decomposition Algorithm for Orientation Estimation of Unmanned Aerial Vehicles

Abstract

Fast orientation estimation of unmanned aerial vehicles is important for maintain stable flight as well as to perform more complex task like obstacle avoidance, search, mapping, etc. The orientation estimation can be performed by means of the fusion of different sensors like accelerometers, gyroscopes and magnetometers, however magnetometers suffer from high distortion in indoor flights, therefore information from cameras can be used as a replacement. This article presents a multilevel decomposition method to process images sent from an unmanned aerial vehicle to a ground station composed by an heterogeneous set of desktop computers. The multilevel decomposition is performed using an alternative hierarchy called Master/Taskmaster/Slaves in order to minimize the network latency. Results shows that using this hierarchy the speed of traditional Master/Slave can be doubled.