Mathematical Model of Errors of Odometry and Georeferencing Channels in Visual Correlation Extreme Navigation

The mathematic model of errors in correlation with the extreme navigation system (CENS) is developed basing on odometry and geo-referencing channels. The realization of the model is done in Simulink, and based on regular and random components of additive noise. The results of simulations prove accumulation of errors for odometry errors and its mitigation in case of geo-referencing in periods of correction

Mathematical Model of Errors of Odometry and Georeferencing Channels in Visual Correlation Extreme Navigation

The mathematic model of errors in correlation with the extreme navigation system (CENS) is developed basing on odometry and geo-referencing channels. The realization of the model is done in Simulink, and based on regular and random components of additive noise. The results of simulations prove accumulation of errors for odometry errors and its mitigation in case of geo-referencing in periods of correction

Survey of computer vision algorithms and applications for unmanned aerial vehicles

This paper presents a complete review of computer vision algorithms and vision-based intelligent applications, that are developed in the field of the Unmanned Aerial Vehicles (UAVs) in the latest decade. During this time, the evolution of relevant technologies for UAVs; such as component miniaturization, the increase of computational capabilities, and the evolution of computer vision techniques have allowed an important advance in the development of UAVs technologies and applications. Particularly, computer vision technologies integrated in UAVs allow to develop cutting-edge technologies to cope with aerial perception difficulties; such as visual navigation algorithms, obstacle detection and avoidance and aerial decision-making. All these expert technologies have developed a wide spectrum of application for UAVs, beyond the classic military and defense purposes. Unmanned Aerial Vehicles and Computer Vision are common topics in expert systems, so thanks to the recent advances in perception technologies, modern intelligent applications are developed to enhance autonomous UAV positioning, or automatic algorithms to avoid aerial collisions, among others. Then, the presented survey is based on artificial perception applications that represent important advances in the latest years in the expert system field related to the Unmanned Aerial Vehicles. In this paper, the most significant advances in this field are presented, able to solve fundamental technical limitations; such as visual odometry, obstacle detection, mapping and localization, et cetera. Besides, they have been analyzed based on their capabilities and potential utility. Moreover, the applications and UAVs are divided and categorized according to different criteria.This research is supported by the Spanish Government through the CICYT projects (TRA2015-63708-R and TRA2013-48314-C3-1-R)

Geo-referencing for UAV Navigation using Environmental Classification

Abstract — A UAV navigation system relying on GPS is vulnerable to signal failure, making a drift free backup system necessary. We introduce a vision based geo-referencing system that uses pre-existing maps to reduce the long term drift. The system classifies an image according to its environmental content and thereafter matches it to an environmentally classified map over the operational area. This map matching provides a measurement of the absolute location of the UAV, that can easily be incorporated into a sensor fusion framework. Experiments show that the geo-referencing system reduces the long term drift in UAV navigation, enhancing the ability of the UAV to navigate accurately over large areas without the use of GPS. I