This paper proposes to take benefit of the simultaneous use of a state observation technique and of an observability/identifiability characterization software to give an integrated answer to the state estimation of nonlinear models. The whole methodology is applicable without any approximation. We motivate and illustrate it on a real system for the estimation of the tyre-slip angle of an automotive.

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.

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.

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.

In order to perform occupancy grid building of in-door environmental for autonomous mobile robot, this paper presents a methodology allowing the merging of beliefs associated to hypotheses represented by binary proposition H like "this particular element belongs to this particular set". In this paper, elements processed are vectors representing points which belong to obstacles. Belief notion is introduced in the combined framework of logic and probability theory and the problem is posed as follows: if different sensors give their own belief in an hypotheses H, how is it possible to merge these believes? Two cases necessary to solve formally occupancy grid building are developed: either sensors characterize different points or sensors characterize the same point. Obtained results are applied to ultrasonic range data fusion.

In order to perform sensor-based map building of an indoor environment for autonomous mobile robots, this paper presents a methodology allowing the merging of degrees of confidence associated to beliefs represented by binary propositions. Such a belief is, by hypothesis, the membership of an element to a given set. The degree of confidence notion is introduced in the combined framework of logic and probability theory. The problem is posed as follows: if different experts give their own degrees of confidence in a belief H, how is it possible to merge these degrees? This paper develops only the case in which experts characterize different elements belonging to the same set. Obtained results are applied to ultrasonic range data fusion.

This paper deals with a temporal multisensor fusion module named Dynamic Data Manager (DDM) developed for the "ProLab 2" demonstrator of the Prometheus European program. It is used in a car, and its goal is to maintain a representation of the dynamic environment needed to inform the driver about possible collisions and risk. The information comes from numerous different perception systems that give asynchronous data. The module is based upon extended Kalman filtering and possibility theory to estimate the current situation, to predict future states and to replace the missing data with a priori estimates based upon a model of the obstacles behaviors.>

This paper is mainly concerned with task function planning in the context of mobile robotics. A task function is basically defined as a closed loop regulation mechanism referenced with respect to a coherent set of map primitives called local map. Task function planning is practically carried out in two steps, First, given an environment model and a sensor model, a geometric path is computed from both the intermediate concept of "configuration uncertainty potential" and the list of all the map primitives used by the vehicle to estimate its localization. Such a path represents a compromise between the length, the simplicity of vehicle control and the tracking accuracy. Then, using an efficient data analysis method, a local map sequence is dynamically computed from the previous path. Finally, task function planning consists in attaching to each local map a closed loop regulation mechanism (task function) so that the complete robot mission can be ensured by successively refering to each local map.>

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.>