Real Time Trajectory Optimization for Vision Based Navigation with Aerobatic Fixed-Wing Vehicles

Fixed-wing unmanned aerial vehicles (UAVS) pose advantages in energy efficiency, endurance, and speed, but also pose disadvantages in maneuverability. These maneuverability challenges can be addressed by exploiting high angle of attack maneuvers. However, navigation with fixed-wing UAVs in constrained spaces is still extremely difficult when the system state and environment are unknown. This essay investigates the use of vision sensors in autonomous navigation of aerobatic fixed-wing UAVs. Perception aware NMPC is explored through the integration of a visibility metric into the trajectory optimization problem. Additionally, a novel frontier-based NMPC method, which improves obstacle avoidance capabilities while mapping, is proposed. These methods are evaluated in a realistic real-time simulation

Unscented Kalman Filter on Lie Groups for Visual Inertial Odometry

International audienceFusing visual information with inertial measurements for state estimation has aroused major interests in recent years. However, combining a robust estimation with computational efficiency remains challenging, specifically for low-cost aerial vehicles in which the quality of the sensors and the processor power are constrained by size, weight and cost. In this paper, we present an innovative filter for stereo visual inertial odometry building on: i) the recently introduced stereo multi-state constraint Kalman filter; ii) the invariant filtering theory; and iii) the unscented Kalman filter (UKF) on Lie groups. Our solution combines accuracy, robustness and versatility of the UKF. We then compare our approach to state-of-art solutions in terms of accuracy, robustness and computational complexity on the EuRoC dataset and a challenging MAV outdoor dataset

Unscented Kalman Filter on Lie Groups for Visual Inertial Odometry

International audienceFusing visual information with inertial measurements for state estimation has aroused major interests in recent years. However, combining a robust estimation with computational efficiency remains challenging, specifically for low-cost aerial vehicles in which the quality of the sensors and the processor power are constrained by size, weight and cost. In this paper, we present an innovative filter for stereo visual inertial odometry building on: i) the recently introduced stereo multi-state constraint Kalman filter; ii) the invariant filtering theory; and iii) the unscented Kalman filter (UKF) on Lie groups. Our solution combines accuracy, robustness and versatility of the UKF. We then compare our approach to state-of-art solutions in terms of accuracy, robustness and computational complexity on the EuRoC dataset and a challenging MAV outdoor dataset

Lifelong localization of robots

This work presents a novel technique for lifelong localization of robots. It performs a tight fusion of GPS and Multi-State Constraint Kalman Filter, a visual-inertial odometry method for robot localization. It is shown in exper- iments that the proposed algorithm achieves better position accuracy than either GPS and Multi-State Constraint Kalman Filter alone. Additionally, the experiments demonstrate that the algorithm is able to reliably operate when the GPS signal is highly corrupted by noise or even in presence of substantial GPS outages. 1Tato práce představuje novou techniku pro celoživotní lokalizaci robotů. Provádí pevné spojení GPS a Multi-State Constraint Kalman Filter, což je metoda vizuální-inerciální odometrie pro lokalizaci robotů. V experimentech je ukázáno, že navrhovaná technika dosahuje lepší přesnosti polohy než GPS nebo Multi-State Constraint Kalman Filter samostatně. Navíc experimenty ukazují, že algoritmus je schopen spolehlivě fungovat, když je signál GPS silně zašuměný nebo dokonce i v případě značných výpadků GPS. 1Katedra teoretické informatiky a matematické logikyDepartment of Theoretical Computer Science and Mathematical LogicFaculty of Mathematics and PhysicsMatematicko-fyzikální fakult