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GRASP Special Seminar: Giuseppe Loianno, University of Naples Federico II, “Visual Navigation, 3D Mapping and Reconstruction for MAVs”
April 19, 2013 @ 2:00 pm - 3:00 pm
Abstract: Giuseppe Loianno’ s research activities focus on the development of sensor fusion algorithms, visual environment reconstruction and visual control for microaerial vehicles (MAVs). Camera sensors are slow for some control applications, thus, it is necessary to fuse their measurements to those of other sensor sources like IMU (Inertial Measurement Unit) and eventually GPS. In the monocular case, the proposed problem has been solved combining different landmarks with IMU measurements to obtain a closed form solution for scale factor estimation. The result has been used with optical flow to control the vehicle along a corridor avoiding lateral obstacles. An interesting application to exploit optical flow, is to use an average flow to control the vehicle in contact with the environment realizing a wall approach for docking and grasping objects. Other sensor fusion techniques based on Kalman filter and Pareto Optimization have been implemented, tested and compared in simulation showing an improvment of the Pareto technique at the price of an increased computational cost. Low-cost range sensors are an attractive alternative for expensive laser scanners or 3D cameras in research domains such as indoor navigation and mapping, surveillance and autonomous robotics. Consumer-grade range sensing technology gives the opportunity to choose between different devices available on the market. The newest ASUS Xtion sensor presents a low weight with respect to the first generation of RGB-D cameras (around 70g without the external casing), it does not need external power other than the USB connection, and it is very compact. These properties give to this device some unique characteristics suitable, for example, for unmanned aerial vehicles applications. The new sensor is employed by coupling a monocular multi-map visual odometry algorithm with depth, estimating the scale factor and obtaining a dense absolute colored map. To avoid memory rising up in large environment, a spatial multi-resolution approach is proposed to acquire point cloud data according to local environment distance. Finally, an environment high level map has been realized for supervisory control and used for estimating a planar wall to be inspected by the vehicle.