Differential GNSS and Vision-Based Tracking to Improve Navigation Performance in Cooperative Multi-UAV Systems

Abstract

Autonomous navigation of micro-UAVs is typically based on the integration of low cost Global Navigation Satellite System (GNSS) receivers and Micro-Electro-Mechanical Systems (MEMS)-based inertial and magnetic sensors to stabilize and control the flight. The resulting navigation performance in terms of position and attitude accuracy may not suffice for other mission needs, such as the ones relevant to fine sensor pointing. In this framework, this paper presents a cooperative UAV navigation algorithm that allows a chief vehicle, equipped with inertial and magnetic sensors, a Global Positioning System (GPS) receiver, and a vision system, to improve its navigation performance (in real time or in the post processing phase) exploiting formation flying deputy vehicles equipped with GPS receivers. The focus is set on outdoor environments and the key concept is to exploit differential GPS among vehicles and vision-based tracking (DGPS/Vision) to build a virtual additional navigation sensor whose information is then integrated in a sensor fusion algorithm based on an Extended Kalman Filter. The developed concept and processing architecture are described, with a focus on DGPS/Vision attitude determination algorithm. Performance assessment is carried out on the basis of both numerical simulations and flight tests. In the latter ones, navigation estimates derived from the DGPS/Vision approach are compared with those provided by the onboard autopilot system of a customized quadrotor. The analysis shows the potential of the developed approach, mainly deriving from the possibility to exploit magnetic- and inertial-independent accurate attitude information.

Development and Experimental Evaluation of a Low-Cost Cooperative UAV Localization Network Prototype

“…To address this issue, various algorithms have been proposed, either for centralized or distributed networks that attempt to estimate the node states under sparse communication [25,42,44,45]. Some of the recent works have provided experimental demonstrations of including camera observations for cooperative localization in a multi-UAV network [46][47][48]. The authors assume presence of identifiable features in the environment (either indoors or outdoors) that can be tracked in a series of images and provide an experimental validation of their system.…”

An Optimized Triad Algorithm for Attitude Determination

“…At last, both two optimal vectors were used to construct the reference and observation matrix. (14) where represent the observation vector after optimizing. From the equations above, the optimal attitude matrix could be finally derived.…”

“…Therefore, two problems need to be considered here: a) the calculation order, b) the wideband noises. Many improvement works have been tried in past research [10][11][12][13][14][15][16]. In [10] an improved TRIAD was proposed by calculating the summation and difference of two vectors, then two new vectors were used in classic TRIAD solving the order problem.…”

“…Therefore, it proposed a new TRIAD algorithm by creating an optimal major vector to solve the different noise problem. In [12][13][14][15][16], TRIAD was performed with Kalman Filter, which increased the computation time. However, neither of them considered both problems that deviated the TRIAD accuracy.…”

Design of Electric Patrol UAVs Based on a Dual Antenna System

“…The collected heading information will be sent to the UAV flight control system through the serial port [3,14,15]. We use heading information from both antennas in the body coordinate system.…”