The practical deployment of wireless positioning systems requires minimizing the calibration procedures while improving the location estimation accuracy. Received Signal Strength localization techniques using propagation channel models are the simplest alternative, but they are usually designed under the assumption that the radio propagation model is to be perfectly characterized a priori. In practice, this assumption does not hold and the localization results are affected by the inaccuracies of the theoretical, roughly calibrated or just imperfect channel models used to compute location. In this paper, we propose the use of weighted multilateration techniques to gain robustness with respect to these inaccuracies, reducing the dependency of having an optimal channel model. In particular, we propose two weighted least squares techniques based on the standard hyperbolic and circular positioning algorithms that specifically consider the accuracies of the different measurements to obtain a better estimation of the position. These techniques are compared to the standard hyperbolic and circular positioning techniques through both numerical simulations and an exhaustive set of real experiments on different types of wireless networks (a wireless sensor network, a WiFi network and a Bluetooth network). The algorithms not only produce better localization results with a very limited overhead in terms of computational cost but also achieve a greater robustness to inaccuracies in channel modeling.

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This paper describes a Mission Definition System and the automated flight process it enables to implement measurement plans for discrete infrastructure inspections using aerial platforms, and specifically multi-rotor drones. The mission definition aims at improving planning efficiency with respect to state-of-the-art waypoint-based techniques, using high-level mission definition primitives and linking them with realistic flight models to simulate the inspection in advance. It also provides flight scripts and measurement plans which can be executed by commercial drones. Its user interfaces facilitate mission definition, pre-flight 3D synthetic mission visualisation and flight evaluation. Results are delivered for a set of representative infrastructure inspection flights, showing the accuracy of the flight prediction tools in actual operations using automated flight control.

Six-degree-of-freedom (6-DoF) pose estimation is of fundamental importance to many applications, such as robotics, indoor tracking and Augmented Reality. Although a number of pose estimation solutions have been proposed, it remains a critical challenge to provide a low-cost, real-time, accurate and easy-todeploy solution. Addressing this issue, this paper describes a multisensor system for accurate pose estimation that relies on low-cost technologies, in particular on a combination of webcams, inertial sensors and a printable colored fiducial. With the aid of inertial sensors, the system can estimate full pose both with monocular and stereo vision. The system error propagation is analyzed and validated by simulations and experimental tests. Our error analysis and experimental data demonstrate that the proposed system has great potential in practical applications, as it achieves high accuracy (in the order of centimeters for the position estimation and few degrees for the orientation estimation) using the mentioned low-cost sensors, while satisfying tight real-time requirements.