Giuseppe Loianno

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An algorithm for autonomous deployment of groups of Micro Aerial Vehicles (MAVs) in the cooperative surveillance task is presented in this paper. The algorithm enables to find a proper distributions of all MAVs in surveillance locations together with feasible and collision free trajectories from their initial position. The solution of the MAV-group(More)
This paper addresses the dynamics, control, planning, and visual servoing for micro aerial vehicles to perform high-speed aerial grasping tasks. We draw inspiration from agile, fast-moving birds, such as raptors, that detect, locate, and execute high-speed swoop maneuvers to capture prey. Since these grasping maneuvers are predominantly in the sagittal(More)
The so-called direct visual SLAM methods have shown a great potential in estimating a semidense or fully dense reconstruction of the scene, in contrast to the sparse reconstructions of the traditional feature-based algorithms. In this paper, we propose for the first time a direct, tightly-coupled formulation for the combination of visual and inertial data.(More)
Consumer grade technology seen in cameras and phones has led to the price/performance ratio of sensors and processors falling dramatically over the last decade. In particular, most devices are packaged with a camera, a gyroscope, and an accelerometer, important sensors for aerial robotics. The low mass and small form factor make them particularly well(More)
We propose a lightweight embedded system for stabilization and control of Unmanned Aerial Vehicles (UAVs) and particularly Micro Aerial Vehicles (MAVs). The system relies solely on onboard sensors to localize the MAV, which makes it suitable for experiments in GPS-denied environments. The system utilizes predictive controllers to find optimal control(More)
It is important to enable micro aerial vehicles to land and perch on different surfaces to save energy by cutting power to motors and to perform tasks such as persistent surveillance. In many cases, the best available surfaces may be vertical windows, walls, or inclined roof tops. In this paper, we present approaches and algorithms for aggressive(More)
The combination of on-board sensors measurements with different statistical characteristics can be employed in robotics for localization and control, especially in GPS-denied environments. In particular, most aerial vehicles are packaged with low cost sensors, important for aerial robotics, such as camera, a gyroscope, and an accelerometer. In this work, we(More)
A new vision-based obstacle avoidance technique for indoor navigation of Micro Aerial Vehicles (MAVs) is presented in this paper. The vehicle trajectory is modified according to the obstacles detected through the Depth Map of the surrounding environment, which is computed online using the Optical Flow provided by a single onboard omnidirectional camera. An(More)
We address the state estimation, control, and planning for aggressive flight with a 150 cm diameter, 250 g quadrotor equipped only with a single camera and an inertial measurement unit (IMU). The use of smartphone grade hardware and the small scale provides an inexpensive and practical solution for autonomous flight in indoor environments. The key(More)
The fusion of IMU and RGB-D sensors presents an interesting combination of information to achieve autonomous localization and mapping using robotic platforms such as ground robots and flying vehicles. In this paper, we present a software framework for cooperative localization and mapping while simultaneously using multiple aerial platforms. We employ a(More)