Chiara Troiani | Joseph Fourier University (original) (raw)
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Papers by Chiara Troiani
IEEE International Conference on Intelligent Robots and Systems, 2012
The SFly project is an EU-funded project, with the goal to create a swarm of autonomous vision co... more The SFly project is an EU-funded project, with the goal to create a swarm of autonomous vision controlled micro aerial vehicles. The mission in mind is that a swarm of MAV's autonomously maps out an unknown environment, computes optimal surveillance positions and places the MAV's there and then locates radio beacons in this environment. The scope of the work includes contributions on multiple different levels ranging from theoretical foundations to hardware design and embedded programming. One of the contributions is the development of a new MAV, a hexacopter, equipped with enough processing power for onboard computer vision. A major contribution is the development of monocular visual SLAM that runs in real-time onboard of the MAV. The visual SLAM results are fused with IMU measurements and are used to stabilize and control the MAV. This enables autonomous flight of the MAV, without the need of a data link to a ground station. Within this scope novel analytical solutions for fusing IMU and vision measurements have been derived. In addition to the realtime local SLAM, an offline dense mapping process has been developed. For this the MAV's are equipped with a payload of a stereo camera system. The dense environment map is used to compute optimal surveillance positions for a swarm of MAV's. For this an optimiziation technique based on cognitive adaptive optimization has been developed. Finally, the MAV's have been equipped with radio transceivers and a method has been developed to locate radio beacons in the observed environment.
This paper investigates the problem of determining the speed and the attitude of a vehicle equipp... more This paper investigates the problem of determining the speed and the attitude of a vehicle equipped with a monocular camera and inertial sensors. The vehicle moves in a 3D unknown environment. It is shown that, by collecting the visual and inertial measurements during a very short time interval, it is possible to determine the following physical quantities: the vehicle speed and attitude, the absolute distance of the point features observed by the camera during the considered time interval and the bias affecting the inertial measurements. In particular, this determination, is based on a closed form solution which analytically expresses the previous physical quantities in terms of the sensor measurements. This closed form determination allows performing the overall estimation in a very short time interval and without the need of any initialization or prior knowledge. This is a key advantage since allows eliminating the drift on the absolute scale and on the vehicle orientation. In addition, the paper provides the minimum number of distinct camera images which are needed to perform this determination. Specifically, if the magnitude of the gravity is unknown, at least four camera images are necessary while if it is a priori known, three camera images are necessary. The performance of the proposed approach is evaluated by using real data.
IEEE International Conference on Intelligent Robots and Systems, 2012
The SFly project is an EU-funded project, with the goal to create a swarm of autonomous vision co... more The SFly project is an EU-funded project, with the goal to create a swarm of autonomous vision controlled micro aerial vehicles. The mission in mind is that a swarm of MAV's autonomously maps out an unknown environment, computes optimal surveillance positions and places the MAV's there and then locates radio beacons in this environment. The scope of the work includes contributions on multiple different levels ranging from theoretical foundations to hardware design and embedded programming. One of the contributions is the development of a new MAV, a hexacopter, equipped with enough processing power for onboard computer vision. A major contribution is the development of monocular visual SLAM that runs in real-time onboard of the MAV. The visual SLAM results are fused with IMU measurements and are used to stabilize and control the MAV. This enables autonomous flight of the MAV, without the need of a data link to a ground station. Within this scope novel analytical solutions for fusing IMU and vision measurements have been derived. In addition to the realtime local SLAM, an offline dense mapping process has been developed. For this the MAV's are equipped with a payload of a stereo camera system. The dense environment map is used to compute optimal surveillance positions for a swarm of MAV's. For this an optimiziation technique based on cognitive adaptive optimization has been developed. Finally, the MAV's have been equipped with radio transceivers and a method has been developed to locate radio beacons in the observed environment.
This paper investigates the problem of determining the speed and the attitude of a vehicle equipp... more This paper investigates the problem of determining the speed and the attitude of a vehicle equipped with a monocular camera and inertial sensors. The vehicle moves in a 3D unknown environment. It is shown that, by collecting the visual and inertial measurements during a very short time interval, it is possible to determine the following physical quantities: the vehicle speed and attitude, the absolute distance of the point features observed by the camera during the considered time interval and the bias affecting the inertial measurements. In particular, this determination, is based on a closed form solution which analytically expresses the previous physical quantities in terms of the sensor measurements. This closed form determination allows performing the overall estimation in a very short time interval and without the need of any initialization or prior knowledge. This is a key advantage since allows eliminating the drift on the absolute scale and on the vehicle orientation. In addition, the paper provides the minimum number of distinct camera images which are needed to perform this determination. Specifically, if the magnitude of the gravity is unknown, at least four camera images are necessary while if it is a priori known, three camera images are necessary. The performance of the proposed approach is evaluated by using real data.