Multi-hypothesis Map-Matching using Particle Filtering

8 pagesInternational audienceThis paper describes a new Map-Matching method relying on the use of Particle Filtering. Since this method implements a multi-hypothesis road-tracking strategy, it is able to handle ambiguous situations arising at junctions or when positioning accuracy is low. In this Bayesian framework, map-matching integrity can be monitored using normalized innovation residuals. An interesting characteristic of this method is its efficient implementation since particles are constraint to the road network; the complexity is reduced to one dimension. Experimental tests carried out with real data are finally reported to illustrate the performance of the method in comparison with a ground truth. The current real-time implementation allows map-matching at 100 Hz with confidence indicators which is relevant for many map-aided ADAS applications

Localization on a vehicle on a precise road map

This article deals with a multisensor based vehicle localization method. The final precision is lesser than one meter. A low cost GPS, a video gray level camera, an odometer and a steer angle sensor provide the data to be fused. The important contributions of the article concern (1) the data fusion by Kalman filtering, (2) the caracterisation of GPS errors and their modelisation by a bias and a low level additive noise and consequently the estimation of the bias and (3) a vision/map coupling to transform local positioning given by a computer vision algorithm into a global reference thus creating another kind of exteroceptive data. The article ends presenting an important experimental validation that corresponds to the implementation of the method in a real driving situation.Cet article présente une approche de fusion multicapteurs permettant d'obtenir la localisation d'un véhicule avec une précision décimétrique. Les différentes sources d'informations utilisées proviennent d'un GPS autonome bas coût, d'une caméra, d'un odomètre et d'un capteur d'angle au volant. Les contributions importantes concernent (1) la formalisation et la résolution du problème de fusion par filtrage de Kalman, (2) la caractérisation expérimentale des erreurs sur les données GPS et consécutivement leur modélisation par un biais et un bruit blanc gaussien additif et l'estimation du biais, (3) le couplage d'une localisation locale par vision avec une carte précise pour fournir une autre source de donnée extéroceptive. L'article se termine par une importante validation expérimentale de la méthode proposée en situation réelle

A UML Profile for Variety and Variability Awareness in Multidimensional Design: An application to Agricultural Robots

Variety and variability are an inherent source of information wealth in schemaless sources, and executing OLAP sessions on multidimensional data in their presence has recently become an object of research. However, all models devised so far propose a ``rigid'' view of the multidimensional content, without taking into account variety and variability. To fill this gap, in this paper we propose V-ICSOLAP, an extension of the ICSOLAP UML profile that supports extensibility and type/name variability for each multidimensional element, as well as complex data types for measures and levels. The real case study we use to motivate and illustrate our approach is that of trajectory analysis for agricultural robots. As a proof-of-concept for V-ICSOLAP, we propose an implementation that relies on the PostgreSQL multi-model DBMS and we evaluate its performances. We also provide a validation of our UML profile by ranking it against other meta-models based on a set of quality metrics

Perception multisensorielle pour la localisation d'un robot mobile en environnement extérieur, application aux véhicules routiers

This thesis report deals with precise and uncorrupted localisation of mobile robots within a road environment associated to a precise numerical map.Le sujet traité dans cette thèse concerne la localisation précise et intègre de robots mobiles ( de véhicules routiers en l'occurence) dans un environnement routier cartographié. Pour ceci, nous proposons une approche multisensorielle hybridant les capteurs classiquement utilisés dans ce genre d'application (un GPS naturel, un odomètre et un gyromètre) avec un système de vision. Ce dernier, en outre, est capable de déterminer très précisement la pose locale du véhicule sur la chaussée en détectant les marquages signalétiques au sol. Il peut donc par l'intermèdiaire de la carte de l'environnement routier, fournir des informations à la fois redondantes et complémentaires à celles fournies par le GPS. Ainsi, cette combinaison d'informations doit garantir la précision et l'intégrité du système de localisation et ce notamment dans les zones urbaines denses où les informations du GPS ne sont pas forcément fiables. La mise en oeuvre de ce système a été menée dans deux études distinctes. Dans la première, la fusion de données est effectuée par un filtre de Kalman étendu . Dans ce cas, la précision obtenue est quasi-décimétrique, cependant la gestion d'hypothèses multiples dans le cadre autoroutier ou des fortes non-linéarités introduites par le système de vision nuisent fortement à l'intégrité des résultats de localisation. C'est donc tout naturellement, que dans la seconde étude, le filtre de Kalman est remplacé par un filtre particulaire et plus particulièrement par un filtre particulaire génétique. La précision obtenue est ici similaire à la première étude et l'intégrité des résultats de la localisation assurée. En revanche, cette méthode est gourmande en temps de calcul et ne peut être utilisée en temps réel sur les ordinateurs actuel

Accurate vehicle positioning onto a numerical map

The road safety is an important research field, one of the principal research topics in this field is the vehicle localization in the road network. This article presents an approach of multisensor fusion able to locate a vehicle with a decimeter precision. The different informations used in this method come from the following sensors: a low cost GPS, a numeric camera, an odometer and a steer angle sensor. Taking into account a complete model of errors on GPS data (bias on position and nonwhite errors) as well as the data provided by an original approach coupling a vision algorithm with a precise numerical map allow us to get this precision

Localisation d'un véhicule sur une carte routière précise

Cet article présente une approche de fusion multicapteurs permettant d'obtenir la localisation d'un véhicule avec une précision décimétrique. Les différentes sources d'informations utilisées proviennent d'un GPS autonome bas coût, d'une caméra, d'un odomètre et d'un capteur d'angle au volant. Les contributions importantes concernent (1) la formalisation et la résolution du problème de fusion par filtrage de Kalman, (2) la caractérisation expérimentale des erreurs sur les données GPS et consécutivement leur modélisation par un biais et un bruit blanc gaussien additif et l'estimation du biais (3) le couplage d'une localisation locale par vision avec une carte précise pour fournir une autre source de données extéroceptive. L'article se termine par une importante validation expérimentale de la méthode proposée en situation réelle

Online Gain Tuning Using Neural Networks: A Comparative Study

This paper addresses the problem of adapting a control system to unseen conditions, specifically to the problem of trajectory tracking in off-road conditions. Three different approaches are considered and compared for this comparative study: The first approach is a classical reinforcement learning method to define the steering control of the system. The second strategy uses an end-to-end reinforcement learning method, allowing for the training of a policy for the steering of the robot. The third strategy uses a hybrid gain tuning method, allowing for the adaptation of the settling distance with respect to the robot’s capabilities according to the perception, in order to optimize the robot’s behavior with respect to an objective function. The three methods are described and compared to the results obtained using constant parameters in order to identify their respective strengths and weaknesses. They have been implemented and tested in real conditions on an off-road mobile robot with variable terrain and trajectories. The hybrid method allowing for an overall reduction of 53.2% when compared with a predictive control law. A thorough analysis of the methods are then performed, and further insights are obtained in the context of gain tuning for steering controllers in dynamic environments. The performance and transferability of these methods are demonstrated, as well as their robustness to changes in the terrain properties. As a result, tracking errors are reduced while preserving the stability and the explainability of the control architecture

Multi-robots trajectory planning for farm field coverage

International audienceIn the last few years, fleets of mobile robots have received increased interest in agriculture with the development of master/slaves control approaches. This paper proposes on the contrary a planning strategy enabling to generate beforehand the trajectory of each robot. For that, the fleet is considered as a single mobile entity with its steering and speed constraints. An admissible trajectory for this virtual entity, including maneuver phases, is generated to cover the shape of a given field. This one is next used to plan the trajectories of the actual robots. A panel of actual fields with different fleets of robots enables to highlight the relevance of the strategy proposed

Online Gain Tuning Using Neural Networks: A Comparative Study

This paper addresses the problem of adapting a control system to unseen conditions, specifically to the problem of trajectory tracking in off-road conditions. Three different approaches are considered and compared for this comparative study: The first approach is a classical reinforcement learning method to define the steering control of the system. The second strategy uses an end-to-end reinforcement learning method, allowing for the training of a policy for the steering of the robot. The third strategy uses a hybrid gain tuning method, allowing for the adaptation of the settling distance with respect to the robot’s capabilities according to the perception, in order to optimize the robot’s behavior with respect to an objective function. The three methods are described and compared to the results obtained using constant parameters in order to identify their respective strengths and weaknesses. They have been implemented and tested in real conditions on an off-road mobile robot with variable terrain and trajectories. The hybrid method allowing for an overall reduction of 53.2% when compared with a predictive control law. A thorough analysis of the methods are then performed, and further insights are obtained in the context of gain tuning for steering controllers in dynamic environments. The performance and transferability of these methods are demonstrated, as well as their robustness to changes in the terrain properties. As a result, tracking errors are reduced while preserving the stability and the explainability of the control architecture

A Multi-Control Strategy to Achieve Autonomous Field Operation

Nowadays, there are several methods of controlling a robot depending on the type of agricultural environment in which it operates. In order to perform a complete agricultural task, this paper proposes a switching strategy between several perception/control approaches, allowing us to select the most appropriate one at any given time. This strategy is presented using an electrical tractor and three control approaches we have developed: path tracking, edge following and furrow pursuing. The effectiveness of the proposed development is tested through full-scale experiments in realistic field environments, performing autonomous navigation and weeding operations in an orchard and an open field. The commutation strategy allows us to select behavior depending on the context, with a good robustness with respect to different sizes of crops (maize and bean). The accuracy stays within ten centimeters, allowing us to expect the use of robots to help with the development of agroecological principles