Dominique Meizel

Virtual sensor: application to vehicle sideslip angle and transversal forces

This paper compares four observers (virtual sensors) of vehicle sideslip angle and lateral forces. The first is linear and uses a linear vehicle model. The remaining observers use an extended nonlinear model. The three nonlinear observers are: extended Luenberger observer, extended Kalman filter and sliding-mode observer. Modeling, model simplification, and observers are described, and an observability analysis is performed for the entire vehicle trajectory. The paper also deals with three different sets of sensors to see the impact of observers results. Comparison is first done by simulation on a valid vehicle simulator, and then observers are used on experimental data. Our study shows that observers are more accurate than simple models as regards unmeasurable variables such as sideslip angle and transversal forces. It also shows that speed of center of gravity is not an indispensable variable here.

Robust Autonomous Robot Localization Using Interval Analysis

This paper deals with the determination of the position and orientation of a mobile robot from distance measurements provided by a belt of onboard ultrasonic sensors. The environment is assumed to be two-dimensional, and a map of its landmarks is available to the robot. In this context, classical localization methods have three main limitations. First, each data point provided by a sensor must be associated with a given landmark. This data-association step turns out to be extremely complex and time-consuming, and its results can usually not be guaranteed. The second limitation is that these methods are based on linearization, which makes them inherently local. The third limitation is their lack of robustness to outliers due, e.g., to sensor malfunctions or outdated maps. By contrast, the method proposed here, based on interval analysis, bypasses the data-association step, handles the problem as nonlinear and in a global way and is (extraordinarily) robust to outliers.

Data fusion of four ABS sensors and GPS for an enhanced localization of car-like vehicles

A localization system using GPS, ABS sensors and a driving wheel encoder is described and tested through real experiments. An odometric technique using the four ABS sensors is presented. Due to the redundancy of the measurements, the precision is better than the one of differential odometry using the rear wheels only. The sampling is performed when necessary and when a GPS measurement is performed. This implies a noticeable reduction of the GPS latency, simplifying thus the data fusion process and improving the quality of its results.

Initial localization by set inversion

In this paper, initial localization problems are solved by using set-membership estimation. The method can be used with any robot and any kind of sensor(s), provided that a computable model of the environment/sensor interaction is available. With a pedagogical aim in mind, it is detailed in the case of the localization of a vehicle from range measurements in a polygonal environment. Salient properties of the method are as follows. First, it does not need any explicit management of matching hypotheses. Second, it is able to deal with ambiguous situations where several radically different vehicle configurations are consistent with the measurements. Third, it can be made robust to outliers. Fourth, it can deal with nonlinear observation models without any approximation. Fifth, the result is guaranteed in the sense that no configuration consistent with the data and the hypotheses can be missed.

About Non-Line-Of-Sight Satellite Detection and Exclusion in a 3D Map-Aided Localization Algorithm

Reliable GPS positioning in city environment is a key issue actually, signals are prone to multipath, with poor satellite geometry in many streets. Using a 3D urban model to forecast satellite visibility in urban contexts in order to improve GPS localization is the main topic of the present article. A virtual image processing that detects and eliminates possible faulty measurements is the core of this method. This image is generated using the position estimated a priori by the navigation process itself, under road constraints. This position is then updated by measurements to line-of-sight satellites only. This closed-loop real-time processing has shown very first promising full-scale test results.

Guaranteed robust nonlinear estimation with application to robot localization

When reliable prior bounds on the acceptable errors between the data and corresponding model outputs are available, bounded-error estimation techniques make it possible to characterize the set of all acceptable parameter vectors in a guaranteed way, even when the model is nonlinear and the number of data points small. However, when the data may contain outliers, i.e., data points for which these bounds should be violated, this set may turn out to be empty, or at least unrealistically small. The outlier minimal number estimator (OMNE) has been designed to deal with such a situation, by minimizing the number of data points considered as outliers. OMNE has been shown in previous papers to be remarkably robust, even to a majority of outliers. Up to now, it was implemented by random scanning, so its results could not be guaranteed. In this paper, a new algorithm based on set inversion via interval analysis provides a guaranteed OMNE, which is applied to the initial localization of an actual robot in a partially known two-dimensional (2-D) environment. The difficult problems of associating range data to landmarks of the environment and of detecting potential outliers are solved as byproducts of the procedure.

Fusion of multi-sensor data: a geometric approach

A geometric approach is presented to solve data fusion problems. The approach uses bounded-error data parameter estimation rather than the usual statistical approach. Updating the location (orientation and position) of a mobile robot in a known polygonal environment is shown as example. The obtained recursive algorithms are similar to those of the Kalman filter with the advantage that only measures improving the estimates are considered.<<ETX>>

Vehicle Localization from Inaccurate Telemetric Data: A Set-Inversion Approach

Abstract In this communication, vehicle localization in a 2D-mapped environment from inaccurate telemetric measurements is stated as a set-inversion problem. A novel methodology based upon a mechanical interpretation of range measurements and interval analysis is presented. The approach aims at characterizing the set Q of all vehicle configurations which are consistent with all range measurements, their bounded associated inaccuracies and the environment model. It provides a global, guaranteed and accurate characterization of the solution set Q. As no a priori matching hypotheses are needed and as the solution set Q can be non connected, the method naturally produces the multiple hypotheses of feature-measurement association.

Force model comparison on the wheel-ground contact for vehicle dynamics

This paper deals with a quantitative comparison between three force models of wheel-ground contact in vehicle dynamics on seven ground types. The first model is analytical and close to the tire. It uses the pressure distribution on the contact area. The second one is also analytical but uses geometric and dynamics characteristics of the vehicle. The last one is empirical. The aim of this paper is to show the validity of the second model because its parameters are available from cheap serial embarked sensors or classical estimation methods such as observers or Kalman filtering.

Planning robust displacement missions by means of robot-tasks and local maps

Abstract This paper introduces a mobile robot mission planning method based on the robot-task concept. Missions are performed by a sequence of closed-loop mechanisms and are thus more robust with respect to the unavoidable differences between reality and the model us the planner. A displacement mission is defined as the combination of a path and of a description of the observations expected along the way, enabling thus to control/check its correct execution. These observations are obtained by the use of local maps defined as coherent sets of landmarks or beacons detectable by the on-board sensing devices. The considered vehicle is a car like one endowed with a rotative telemeter. The environment is 2D and is structured by polygonal obstacles and landmarks. Robot-task generation is carried out in three phases. The first phase consist of defining a path as a set of configurations. Each of these configurations is attached to the list of map primitives providing the robot with the best localization information. This part is a compromise between length, safety conditions and complexity of vehicle control. The second phase applies a data analysis method to the pain, in order to dynamically group path configurations ssociated to the same list of primitives called local maps. The third phase precisely deals with robot-task definition. It first consists in defining a smooth path (Bezier curve) from each group of configurations. The robot-task sequence correspond to the ordered list of these pathsassociated with their respective local maps.