Invariant EKF Design for Scan Matching-aided Localization

Localization in indoor environments is a technique which estimates the robot's pose by fusing data from onboard motion sensors with readings of the environment, in our case obtained by scan matching point clouds captured by a low-cost Kinect depth camera. We develop both an Invariant Extended Kalman Filter (IEKF)-based and a Multiplicative Extended Kalman Filter (MEKF)-based solution to this problem. The two designs are successfully validated in experiments and demonstrate the advantage of the IEKF design

The Invariant Unscented Kalman Filter

International audienceThis article proposes a novel approach for nonlinear state estimation. It combines both invariant observers theory and unscented filtering principles whitout requiring any compatibility condition such as proposed in the -IUKF algorithm. The resulting algorithm, named IUKF (Invariant Unscented Kalman Filter), relies on a geometrical-based constructive method for designing filters dedicated to nonlinear state estimation problems while preserving the physical invariances and systems symmetries. Within an invariant framework, this algorithm suggests a systematic approach to determine all the symmetry- preserving terms without requiring any linearization and highlighting remarkable invariant properties. As a result, the estimated covariance matrices of the IUKF converge to quasi-constant values due to the symmetry-preserving property provided by the invariant framework. This result enables the development of less conservative robust control strategies. The designed IUKF method has been successfully applied to some relevant practical problems such as the estimation of attitude for aerial vehicles using low-cost sensors reference systems. Typical experimental results using a Parrot quadrotor are provided in this pape

On the Covariance of ICP-based Scan-matching Techniques

This paper considers the problem of estimating the covariance of roto-translations computed by the Iterative Closest Point (ICP) algorithm. The problem is relevant for localization of mobile robots and vehicles equipped with depth-sensing cameras (e.g., Kinect) or Lidar (e.g., Velodyne). The closed-form formulas for covariance proposed in previous literature generally build upon the fact that the solution to ICP is obtained by minimizing a linear least-squares problem. In this paper, we show this approach needs caution because the rematching step of the algorithm is not explicitly accounted for, and applying it to the point-to-point version of ICP leads to completely erroneous covariances. We then provide a formal mathematical proof why the approach is valid in the point-to-plane version of ICP, which validates the intuition and experimental results of practitioners.Comment: Accepted at 2016 American Control Conferenc

Reducing Computational Cost in the Invariant Unscented Kalman Filtering For Attitude Estimation

This article proposes a new formulation to derive the invariant unscented Kalman filter (IUKF) algorithm for attitude estimation problem, where both state and sigma-point are considered as a transformation group parametrization of the filter. The detailed IUKF equations are presented in this article. The filter equations relie on the same ideas as the usual Unscented Kalman Filter (UKF), but it uses a geometrically adapted correction term based on an invariant output error. The specific interest of the proposed formulation is that only the invariant state estimation errors between the predicted state and each sigma point must be known to determine the predicted outputs errors. As we have already computed the set of invariant state errors during the prediction step, the computation cost to find the covariance matrix of the invariant state estimation in the update step is greatly reduced

Augmented Invariant-EKF designs for simultaneous state and disturbance estimation

In this thesis, we study Invariant-EKF designs for invariant systems with disturbances. We identify two sets of sufficient conditions that preserve the invariance of systems when additive dynamic disturbances are applied. A first order approximation of the filtering covariance matrices is proposed that more accurately represents the uncertainties for the Invariant-EKF. Applying the developed theory, three different IEKF designs are presented for a unicycle robot under linear disturbances. Monte Carlo simulations demonstrate the contribution of the first order approximation and also illustrate the performance improvement of all three designs over the standard Extended Kalman Filter

Desenvolvimento e validação de um simulador 3D para prova de condução autónoma do FNR

One of the more relevant research challenge competitions in the Festival Nacional de Robótica (FNR) is the Autonomous Driving Competition, where fully autonomous robots have to navigate in a complex scenario replicating road environments. Here robots must overcome challenges ranging from perception to navigation and control. In addition, there are challenges associated to the integration of sensors (Cameras, Inertial Measurement Unit (IMU), Laser and Encoders) able to support the navigation task or even at the mechanic development level, which sometimes, can be difficult to address by some research groups. Therefore, this paper presents a simulation environment developed for FNR Autonomous Driving Competition. This paper also covers various topics of mobile robotics, the proposed simulator is used in the development and validation of a location system, trajectory tracking, and obstacle avoidance.Inserida no Festival Nacional de Robótica (FNR), a prova Autonomous Driving Competition endereça desafios da condução autónoma, nesta competição os sistemas robóticos totalmente autónomos devem navegar num cenário de prova que retrata condições do ambiente rodoviário. Os robôs devem superar desafios relevantes da robótica móvel como a localização, percepção, navegação e controlo. São diversas as dificuldades e condicionalismos por parte dos participantes a este modelo de competição. A participação carece de uma solução de robô nem sempre disponível ou de fácil desenvolvimento e tende a integrar um considerável número de sensores. Esta dissertação apresenta um ambiente de simulação da prova de Condução Autónoma do FNR, sendo o simulador proposto utilizado no desenvolvimento e validação de um módulo de localização, path traking, path following e obstacle avoidance

Secure indoor navigation and operation of mobile robots

In future work environments, robots will navigate and work side by side to humans. This raises big challenges related to the safety of these robots. In this Dissertation, three tasks have been realized: 1) implementing a localization and navigation system based on StarGazer sensor and Kalman filter; 2) realizing a human-robot interaction system using Kinect sensor and BPNN and SVM models to define the gestures and 3) a new collision avoidance system is realized. The system works on generating the collision-free paths based on the interaction between the human and the robot.In zukünftigen Arbeitsumgebungen werden Roboter navigieren nebeneinander an Menschen. Das wirft Herausforderungen im Zusammenhang mit der Sicherheit dieser Roboter auf. In dieser Dissertation drei Aufgaben realisiert: 1. Implementierung eines Lokalisierungs und Navigationssystem basierend auf Kalman Filter: 2. Realisierung eines Mensch-Roboter-Interaktionssystem mit Kinect und AI zur Definition der Gesten und 3. ein neues Kollisionsvermeidungssystem wird realisiert. Das System arbeitet an der Erzeugung der kollisionsfreien Pfade, die auf der Wechselwirkung zwischen dem Menschen und dem Roboter basieren