On-the-field calibration of an array of sensors

International audienceWe address the problem of the calibration of an array of sensors by investigating theoretically and experimentally the case of 2 three-axis sensors. Our focus is on magnetometers that can be used in a low-cost inertial navigation system. Usual errors (misalignments, non-orthogonality, scale factors, biases) are accounted for. The proposed calibration method requires no specific calibration hardware. Instead, we solely use the fact that, if the sensor is properly calibrated, the norm of the sensed field must remain constant irrespective of the sensors orientation. Several strategies of calibration for an array of sensors are described along with the impact of (unavoidable) field disturbances. Experiments conducted with a couple of magneto-resistive magnetometers and data fusion results illustrate the relevance of the approach

Using magnetic disturbances to improve IMU-based position estimation

International audienceWe address the problem of position estimation for a rigid body using an inertial measurement unit (IMU). In this paper, we present a Kalman filtering technique which takes advantage of the magnetic disturbances usually observed indoors. This is an important topic for military operations in urban areas where GPS is often unavailable. This approach yields significant improvements in estimation accuracy. We illustrate our technique with several experimental results obtained with a low cost IMU

Iterative calibration method for inertial and magnetic sensors

International audienceWe address the problem of three-axis sensor calibration. Our focus is on magnetometers. Usual errors (misalignment, non-orthogonality, scale factors, biases) are accounted for. We consider a method where no specific calibration hardware is required. We solely use the fact that the norm of the sensed field must remain constant irrespective of the sensors orientation. The proposed algorithm is iterative. Its convergence is studied. Experiments conducted with MEMS sensors (magnetometers) stress the relevance of the approach

Solution de guidage-navigation-pilotage pour véhicules autonomes hétérogènes en vue d'une mission collaborative

The theme of "low-cost navigation", characterized by transferring performance from sensors to data fusion and control algorithms, is a central theme for many military applications, in particular those related to light troops. Using inertial or magnetic sensors from the MEMS category, along with civilian GPS, optimizes weight, size and energy consumption. As will be demonstrated, this approach requires application-specific guidance navigation and control algorithms to compensate for the relatively poor quality of the sensor signals. In this thesis, we consider three scenarios of interest and develop innovative techniques for guidance and navigation. First, we consider the case of a ground vehicle equipped with proximity sensors, a gyroscope, odometers and a GPS. Experimentally, we implement an algorithm for obstacle avoidance for which we establish a proof of convergence, and an offline path-planning algorithm that is complemented by a dynamic feedback controller and a non-linear state estimator. Then, we investigate the case of an unstable helicopter. We develop and implement algorithms within a real-time control system that we designed specifically for computational and estimation tasks. The state estimator includes a model of the flight dynamics, and uses data from inertial sensors, a barometer, and a GPS to serve as input for a closed-loop controller. The parameters of the model are identified accurately by using data obtained during experimental flights. Eventually, we shall perform autonomous hovering flights, which stress the performance of the system. Finally, we consider the problem of a pedestrian walking inside buildings. The heading estimation errors observed using various platforms (both ground and aerial) suggest a new use of the magnetic field, and we propose to derive information by inspecting its gradients. We explain how to use (unknown) disturbances of the magnetic field to improve the position estimate in inertial navigation. Experimental results emphasize the relevance of the approach.Le thème de la « navigation bas-coût », caractérisé par le transfert de performance des capteurs vers les algorithmes de fusion de données et de commande, est un thème central dans de nombreuses applications militaires liées en particulier aux besoins nouveaux des troupes légères. L'utilisation de capteurs inertiels ou magnétométriques type MEMS et de GPS civils (notamment), optimise les critères d'encombrement, de masse et de consommation. Mais elle requiert, en compensation des performances relativement médiocres des capteurs, des algorithmes de guidage-navigation-pilotage spécifiques aux applications considérées. Dans ce mémoire, nous considérons trois scénarios d'utilisation et exposons des techniques innovantes pour la localisation et le contrôle. Nous considérons d'abord le cas d'un robot terrestre équipé de capteurs de proximité, d'un gyroscope, d'odomètres et d'un GPS. Nous implémentons expérimentalement avec succès un algorithme d'évitement d'obstacles dont nous établissons une preuve de convergence, ainsi qu'un algorithme de planification de trajectoires hors ligne dont nous utilisons les résultats en temps réel pour réaliser, via un estimateur non linéaire, un bouclage par retour dynamique. Nous étudions ensuite le cas du vol autonome d'une plate-forme aérienne instable de type hélicoptère. Nous développons et implémentons, à bord sur un système de mesure et de calcul temps-réel de notre conception, un estimateur d'état incluant un modèle de la dynamique du vol de l'engin, recalé par les capteurs inertiels, barométriques, et GPS ainsi qu'un bouclage par retour d'état. En utilisant les résultats de filtrage sur les vols effectués, les paramètres du modèle sont précisément identifiés : la qualité et la robustesse de l'estimation obtenues grâce au modèle permettent de réaliser un vol stationnaire autonome en extérieur. Enfin nous considérons le problème d'un piéton évoluant à l'intérieur de bâtiments. Les erreurs d'estimation du cap lors de l'utilisation des différentes plate-formes (terrestre comme aérienne) nous guident vers une utilisation nouvelle du champ magnétique par l'inspection de ses gradients. Par une technique que nous exposons, nous montrons comment utiliser les perturbations (inconnues) du champ magnétique pour améliorer considérablement l'estimation de position d'une centrale inertielle bas-coût au point qu'elle devienne un instrument de localisation

Using distributed magnetometers to increase IMU-based velocity estimation into perturbed area

International audienceWe address the problem of position estimation for a rigid body using an inertial measurement unit (IMU) and a set of spatially distributed magnetometers. We take advantage of the magnetic field disturbances usually observed indoors. This is particularly relevant when GPS is unavailable (e.g. during military operations in urban areas). We illustrate our technique with several experimental results obtained with a Kalman filter. We also present our testing bench which consists of low cost sensors (IMU and magnetometers)

Experiments of trajectory generation and obstacle avoidance for a UGV

International audienceIn this paper, we report results of investigations conducted on a mobile robotic experiment developed at the DGA Laboratoire de Recherche en Balistique et Aérodynamique in a joint work with Ecole des Mines. The vehicles under consideration are in fact similar to unicycles. We investigate a flatness based approach (combining open-loop optimization and closed loop tracking) and gyroscopic forces control laws. Experimental results are exposed. A theoretical proof of obstacle avoidance for a gyroscopic scheme is presented

Un filtre de navigation Indoor/Outdoor robuste par fusion de données vision et magnéto-inertielles

International audienceVisual-inertial Navigation Systems (VINS) are nowadays used for robotic or augmented reality applications. They aim to compute the motion of the robot or the pedestrian in an environment that is unknown and does not have specific localization infrastructure. Because of the low quality of inertial sensors that can be used reasonably for these two applications, state of the art VINS rely heavily on the visual information to correct at high frequency the drift of inertial sensors integration. These methods struggle when environment does not provide usable visual features, such than in low-light of texture-less areas. In the last few years, some work have been focused on using an array of magnetometers to exploit opportunistic stationary magnetic disturbances available indoor in order to deduce a velocity. This led to Magneto-inertial Dead-reckoning (MI-DR) systems that show interesting performance in their nominal conditions, even if they can be defeated when the local magnetic gradient is too low, for example outdoor. We propose in this work to fuse the information from a monocular camera with the MI-DR technique to increase the robustness of both traditional VINS and MI-DR itself. We use an inverse square root filter inspired by the MSCKF algorithm and describe its structure thoroughly in this paper. We show navigation results on a real dataset captured by a sensor fusing a commercial-grade camera with our custom MIMU (Magneto-inertial Measurment Unit) sensor. The fused estimate demonstrates higher robustness compared to pure VINS estimate, specially in areas where vision is non informative. These results could ultimately increase the working domain of mobile augmented reality systems

Timestamping for an array of low-cost sensors

This paper describes a technique for timestamping data from an array of sensors. This is a challenging issue that needs to be solved for the magnetometry-based navigation system presented here. This system uses finite difference schemes to evaluate spatial and time derivatives of a vector field. The accuracy of the timestamping method is critical. In the context of this particular application, we detail the various difficulties that must be circumvented. We present a software architecture, which was run on an experimental device