Map matching is defined as the process of correlating two sets of geographical positional information. Combined to dead-reckoning estimation approach, the map-matching improves significantly the overall positioning accuracy. In this paper, a new map-matching is proposed. This approach is derived from the Hidden Markov Model (HMM) theory and tailored to map-matching technique. The efficiency of the adopted approach is demonstrated thanks to the use of an inertial positioning system over large experiments. The adopted approach shows that improvement is achieved in terms of positioning accuracy as well as in computation cost compared to common techniques.

This paper presents a 3D model-based tracking suitable for indoor position control of an unmanned aerial vehicle (UAV). Given a 3D model of the edges of its environment, the UAV locates itself thanks to a robust multiple hypothesis tracker. The pose estimation is then fused to inertial data to provide the translational velocity required for the control. A hierarchical control is used to achieve positioning tasks. Experiments on a quad-rotor aerial vehicle validate the proposed approach.

This paper presents a vision-based navigation strategy for a vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV) using a single embedded camera observing natural landmarks. In the proposed approach, images of the environment are first sampled and stored as a set of ordered key images (visual path) and organized providing a visual memory of the environment. The robot navigation task is then defined as a concatenation of visual path subsets (called visual route) linking the current observed image and a target image belonging to the visual memory. The UAV is controlled to reach each image of the visual route using a vision-based control law adapted to its dynamic model and without explicitly planning any trajectory. This framework is largely substantiated by experiments with a X4-flyer equipped with a fisheye camera.

In this paper, we investigate a range of image-based visual servo control algorithms for regulation of the position of a quadrotor aerial vehicle. The most promising control algorithms have been successfully implemented on an autonomous aerial vehicle and demonstrate excellent performance.

An image-based visual servo control is presented for an unmanned aerial vehicle (UAV) capable of stationary or quasi-stationary flight with the camera mounted onboard the vehicle. The target considered consists of a finite set of stationary and disjoint points lying in a plane. Control of the position and orientation dynamics is decoupled using a visual error based on spherical centroid data, along with estimations of the linear velocity and the gravitational inertial direction extracted from image features and an embedded inertial measurement unit. The visual error used compensates for poor conditioning of the image Jacobian matrix by introducing a nonhomogeneous gain term adapted to the visual sensitivity of the error measurements. A nonlinear controller, that ensures exponential convergence of the system considered, is derived for the full dynamics of the system using control Lyapunov function design techniques. Experimental results on a quadrotor UAV, developed by the French Atomic Energy Commission, demonstrate the robustness and performance of the proposed control strategy.

An image-based visual servo control is presented for an unmanned aerial vehicle (UAV) capable of stationary or quasi-stationary flight. The proposed control design addresses visual servo of 'eye-in-hand' type systems. The control of the position and orientation dynamics are decoupled using a visual error based on a spherical centroid data, along with estimation of the gravitational inertial direction. The error used compensates for the poor conditioning of the Jacobian matrix seen in earlier work in this area by introducing a non-homogeneous gain term adapted to the visual sensitivity of the error measurements. A nonlinear controller is derived for the full dynamics of the system. Experimental results on an experimental UAV known as an X4-flyer made by the French Atomic Energy Commission (CEA) demonstrate the robustness and performances of the proposed control strategy.

In this paper, we present a control design for the teleoperation of a miniature unmanned aerial vehicle known as an X4-flyer. A simple dynamic nonlinear model for the vehicle, valid for quasi-stationary flight conditions, is derived as a basis for the control design. An attitude control based on information issued from an inertial measurement unit is designed. In order to control the vehicle altitude, an adaptive controller avoiding the ground effects and based on measurements issued from an ultrasonic low cost sensor is designed. In order to compute the altitude velocity, an estimator based on the proposed modelling is used. At the end of the paper, experimental results are presented