Cooperative navigation and visual tracking with passive ranging for UAV flight in GNSS-challenging environments

This paper discusses an approach conceived to improve navigation performance of small Unmanned Aerial Vehicles (UAVs) in GNSS-challenging environments by exploiting cooperation with other aircraft flying in better GNSS coverage conditions. Cooperation is realized by exchanging navigation data (i.e., GNSS observables when available) and exploiting a monocular camera system for relative vision-based tracking. Cooperative measurements are used within an Extended Kalman Filter, developing a solution potentially ready for real-time applications. The visual algorithm exploits both Deep Learning-based detectors and standard machine vision techniques to provide not only accurate line-of-sight but also distance estimates, and it is designed to deal with targets placed both above and below the horizon. The two algorithmic blocks are integrated in a closed loop fashion since navigation estimates are used in feedback to support visual processing. An experimental flight test campaign is carried out using two quadcopters to assess attainable navigation performance in terms of attitude and positioning. Results compare filter performance when using line-of-sight only with the case of using line-of-sight and ranging measurements altogether. They demonstrate that reliability and integrity of visual algorithms are good enough for the navigation filter needs, and that metric positioning error is achieved within GNSS-challenging areas by using the proposed cooperative strategy. The added value of range estimation strongly depends on the formation geometry and the GNSS coverage conditions.