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Giuseppe Loianno
Researcher at New York University
Publications - 114
Citations - 3309
Giuseppe Loianno is an academic researcher from New York University. The author has contributed to research in topics: Computer science & Inertial measurement unit. The author has an hindex of 28, co-authored 94 publications receiving 2243 citations. Previous affiliations of Giuseppe Loianno include University of Pennsylvania & Information Technology University.
Papers
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Proceedings ArticleDOI
Cooperative localization and mapping of MAVs using RGB-D sensors
TL;DR: A monocular visual odometry algorithm is employed to solve the localization task, where the depth data flow associated to the RGB image is used to estimate the scale factor associated with the visual information.
Proceedings ArticleDOI
Visual inertial odometry for quadrotors on SE(3)
TL;DR: This work develops a visual inertial odometry system based on the Unscented Kalman Filter acting on the Lie group SE(3) to obtain an unique, singularity-free representation of a rigid body pose and presents experimental results to show the effectiveness of the proposed approach for state estimation of a quadrotor platform.
Proceedings ArticleDOI
Embedded model predictive control of unmanned micro aerial vehicles
TL;DR: A lightweight embedded system for stabilization and control of Unmanned Aerial Vehicles and particularly Micro Aerial Vehicles, which relies solely on onboard sensors to localize the MAV, which makes it suitable for experiments in GPS-denied environments.
Proceedings ArticleDOI
Smartphones power flying robots
Giuseppe Loianno,Yash Mulgaonkar,Christopher Brunner,Dheeraj Ahuja,Arvind Ramanandan,Murali Ramaswamy Chari,Serafin Diaz,Vijay Kumar +7 more
TL;DR: This work presents the first fully autonomous smartphone-based quadrotor, running on an off-the-shelf smartphone, with all the software functionality in a smartphone app, to be used for autonomous flight with small flying robots in indoor buildings.
Proceedings ArticleDOI
MAV indoor navigation based on a closed-form solution for absolute scale velocity estimation using Optical Flow and inertial data
TL;DR: A new vision-based obstacle avoidance technique for indoor navigation of Micro Aerial Vehicles (MAVs) and a dynamic region-of-interest for image features extraction and a self-limitation control for the navigation velocity are proposed to improve safety in view of the estimated vehicle velocity.