On assiste aujourd'hui à l'importation des NTIC (Nouvelles Technologies de l'Information et de la Télécommunication) dans la robotique. L'union de ces technologies donnera naissance, dans les années à venir, à la robotique de service grand-public.Cet avenir, s'il se réalise, sera le fruit d'un travail de recherche, amont, dans de nombreux domaines : la mécatronique, les télécommunications, l'automatique, le traitement du signal et des images, l'intelligence artificielle ... Un des aspects particulièrement intéressant en robotique mobile est alors le problème de la localisation et de la cartographie simultanée. En effet, dans de nombreux cas, un robot mobile, pour accéder à une intelligence, doit nécessairement se localiser dans son environnement. La question est alors : quelle précision pouvons-nous espérer en terme de localisation? Et à quel coût?Dans ce contexte, un des objectifs de tous les laboratoires de recherche en robotique, objectif dont les résultats sont particulièrement attendus dans les milieux industriels, est un positionnement et une cartographie de l'environnement, qui soient à la fois précis, tous-lieux, intègre, bas-coût et temps-réel. Les capteurs de prédilection sont les capteurs peu onéreux tels qu'un GPS standard (de précision métrique), et un ensemble de capteurs embarquables en charge utile (comme les caméras-vidéo). Ce type de capteurs constituera donc notre support privilégié, dans notre travail de recherche. Dans cette thèse, nous aborderons le problème de la localisation d'un robot mobile, et nous choisirons de traiter notre problème par l'approche probabiliste. La démarche est la suivante, nous définissons nos 'variables d'intérêt' : un ensemble de variables aléatoires. Nous décrivons ensuite leurs lois de distribution, et leur modèles d'évolution, enfin nous déterminons une fonction de coût, de manière à construire un observateur (une classe d'algorithme dont l'objectif est de déterminer le minimum de notre fonction de coût). Notre contribution consistera en l'utilisation de mesures GPS brutes GPS (les mesures brutes - ou raw-datas - sont les mesures issues des boucles de corrélation de code et de phase, respectivement appelées mesures de pseudo-distances de code et de phase) pour une navigation bas-coût précise en milieu extérieur suburbain. En utilisant la propriété dite 'entière' des ambiguïtés de phase GPS, nous étendrons notre navigation pour réaliser un système GPS-RTK (Real Time Kinematic) en mode différentiel local précise et bas-coût. Nos propositions sont validées par des expérimentations réalisées sur notre démonstrateur robotique. / We are witnessing nowadays the importation of ICT (Information and Communications Technology) in robotics. These technologies will give birth, in upcoming years, to the general public service robotics. This future, if realised, shall be the result of many research conducted in several domains: mechatronics, telecommunications, automatics, signal and image processing, artificial intelligence ... One particularly interesting aspect in mobile robotics is hence the simultaneous localisation and mapping problem. Consequently, to access certain informations, a mobile robot has, in many cases, to map/localise itself inside its environment. The following question is then posed: What precision can we aim for in terms of localisation? And at what cost?In this context, one of the objectives of many laboratories indulged in robotics research, and where results impact directly the industry, is the positioning and mapping of the environment. These latter tasks should be precise, adapted everywhere, integrated, low-cost and real-time. The prediction sensors are inexpensive ones, such as a standard GPS (of metric precision), and a set of embeddable payload sensors (e.g. video cameras). These type of sensors constitute the main support in our work.In this thesis, we shed light on the localisation problem of a mobile robot, which we choose to handle with a probabilistic approach. The procedure is as follows: we first define our "variables of interest" which are a set of random variables, and then we describe their distribution laws and their evolution models. Afterwards, we determine a cost function in such a manner to build up an observer (an algorithmic class where the objective is to minimize the cost function).Our contribution consists of using brute GPS measures (brute measures or raw datas are measures issued from code and phase correlation loops, called pseudo-distance measures of code and phase, respectively) for a low-cost navigation, which is precise in an external suburban environment. By implementing the so-called "whole" property of GPS phase ambiguities, we expand the navigation to achieve a GPS-RTK (Real-Time Kinematic) system in a precise and low-cost local differential mode.Our propositions has been validated through experimentations realized on our robotic demonstrator.

"This work presents a novel method to efficiently factorize the combination of multiple factor graphs having common variables of estimation. The fast-paced innovation in the algebraic graph theory has enabled new tools of state estimation like factor graphs. Recent factor graph formulation for Simultaneous Localization and Mapping (SLAM) like Incremental Smoothing and Mapping using the Bayes tree (ISAM2) has been very successful and garnered much attention. Variable ordering, a well-known technique in linear algebra is employed for solving the factor graph. Our primary contribution in this work is to reuse the variable ordering of the graphs being combined to find the ordering of the fused graph. In the case of mapping, multiple robots provide a great advantage over single robot by providing a faster map coverage and better estimation quality. This coupled with an inevitable increase in the number of robots around us produce a demand for faster algorithms. For example, a city full of self-driving cars could pool their observation measurements rapidly to plan a traffic free navigation. By reusing the variable ordering of the parent graphs we were able to produce an order-of-magnitude difference in the time required for solving the fused graph. We also provide a formal verification to show that the proposed strategy does not violate any of the relevant standards. A common problem in multi-robot SLAM is relative pose graph initialization to produce a globally consistent map. The other contribution addresses this by minimizing a specially formulated error function as a part of solving the factor graph. The performance is illustrated on a publicly available SuiteSparse dataset and the multi-robot AP Hill dataset."

<p>The goal of this work has been to improve the accuracy of a pre-existing algorithm for vehicle pose estimation, which uses intrinsic measurements of vehicle motion and measurements derived from far infrared images.</p><p>Estimating the pose of a vehicle, based on images from an on-board camera and intrinsic measurements of vehicle motion, is a problem of simultanoeus localization and mapping (SLAM), and it can be solved using the extended Kalman filter (EKF). The EKF is a causal filter, so if the pose estimation problem is to be solved off-line acausal methods are expected to increase estimation accuracy significantly. In this work the EKF has been compared with an acausal method for solving the SLAM problem called smoothing and mapping (SAM) which is an optimization based method that minimizes process and measurement noise.</p><p>Analyses of how improvements in the vehicle motion model, using a number of different model extensions, affects accuracy of pose estimates have also been performed.</p>

The goal of this work has been to improve the accuracy of a pre-existing algorithm for vehicle pose estimation, which uses intrinsic measurements of vehicle motion and measurements derived from far infrared images. Estimating the pose of a vehicle, based on images from an on-board camera and intrinsic measurements of vehicle motion, is a problem of simultanoeus localization and mapping (SLAM), and it can be solved using the extended Kalman filter (EKF). The EKF is a causal filter, so if the pose estimation problem is to be solved off-line acausal methods are expected to increase estimation accuracy significantly. In this work the EKF has been compared with an acausal method for solving the SLAM problem called smoothing and mapping (SAM) which is an optimization based method that minimizes process and measurement noise. Analyses of how improvements in the vehicle motion model, using a number of different model extensions, affects accuracy of pose estimates have also been performed.

The graph-based formulation of the navigation problem is establishing itself as one of the standard ways to formulate the navigation problem within the sensor fusion community. It enables a convenient way to access information from previous positions which can be used to enhance the estimate of the current position.To restrict working memory usage, map partitioning can be used to store older parts of the map on a hard drive, in the form of submaps. This limits the number of previous positions within the active map. This thesis examines the effect that map partitioning information loss has on the state of the art positioning algorithm iSAM2, both in open routes and when loop closure is achieved. It finds that larger submaps appear to cause a smaller positional error than smaller submaps for open routes. The smaller submaps seem to give smaller positional error than larger submaps when loop closure is achieved. The thesis also examines how the density of landmarks at the partition point affects the positional error, but the obtained result is mixed and no clear conclusions can be made. Finally it reviews some loop closure detection algorithms that can be convenient to pair with the iSAM2 algorithm.