This paper presents the development of a localization system in GPS-denied environments using an Inertial Measurement Unit (IMU) and a Pulse-Doppler radar. A ground speed estimation from radar measurements is first proposed. This estimation is combined with noisy measurements from an IMU in a Luenberger observer, allowing accurate deadreckoning. The methodology proposed provides short-term position of the sensors embedded in a white cane, the ultimate goal being obstacle detection through the computation of a model of the surroundings. The results show that this solution gives an error growth rate of the position estimation of 0.026m/s, which is a hundred times better than the one obtained with the naive double integration of the accelerometer data.
In the context of autonomous navigation, the vehicle trajectory estimation and the detection of surrounding obstacles are two critical functionalities that must be robust to difficult environmental conditions (e.g. fog, dust, snow) and the unavailability of infrastructure signals (e.g. GPS). With the advantage of remaining operable in low-visibility conditions, radar sensors are good candidates to detect obstacles in an autonomous navigation context. In this paper, we show that radars can also be successfully used for real-time trajectory estimation. We address the case of an autonomous micro-drone intended for the exploration of piping networks and embedding a Frequency Modulated Continuous Waves (FMCW) MIMO radar. We show that using a beamforming technique to virtually steer the radar field-of-view, we can simultaneously estimate the horizontal and vertical velocity of the drone as well as its height. These results are first validated through simulations based on experimental drone flight data and a radar simulator. Then, using an Infineon 77GHz FMCW radar, we show through real-world experiments the high performance attainable with our solution.
In this paper, we present a numerical method based the Lyapunov theory to estimate the attraction domain of a class of nonlinear systems. This problem is motivated by the analysis of linear attitude controllers for the control of Vertical Take-Off and Landing (VTOL) vehicles such as quadrotors. These linear controllers are typically designed in order to ensure local stability around the hover point. The purpose of this work is to estimate their attraction domain around this point. The proposed attraction domain estimation method requires to solve a convex optimization problem involving parameter-dependent Linear Matrix Inequalities (LMI). This problem is generally difficult to solve as it is an infinite dimensional optimization problem. However, we reveal that the specific structure of the VTOL vehicles attitude model can be exploited to make this LMI problem finite dimensional and thus numerically solvable.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.