This article concerns road safety and driving assistance. To solve this problem, we propose a data fusion architecture based on the Dempster-Shafer theory. This multilevel approach allows the management of complementary and redundant data which come from two perception systems: an omnidirectional vision sensor and a laser telemeter. The originality of this architecture is its ability to manage and propagate uncertainties from low level data until a high level information of danger given to the driver. The first part concerns the data sensor. The second part deals with the quantification of the uncertainties of the detected vehicles, followed by a determination of situations of danger and the evaluation of their level of dangerousness with the aim of supplying the driver with an indicator of global danger around the vehicle.

This article deals about uncertainty and imprecision treatment during the mobile robot localization process. The imprecision determination is based on the use of the interval formalism. The mobile robot is equipped with an exteroceptive sensor and odometers. The imprecise data given by these two sensors are fused by constraint propagation on intervals. At the end of the algorithm, we get 3D localization subpaving which is supposed to contain in a guaranteed way the robot’s position. Concerning the uncertainty, it is managed throw a propagation architecture based on the used of the Transferable Belief Model of Smets.

In this article, a multi-sensor cooperation approach between an omnidirectional vision system and a panoramic range finder is presented. The aim is to localize dynamically a mobile robot. These two sensors provide some complementary and redundant data which enable to construct a robust sensorial model which integrates all important number of significant primitives. Based on this model, we treat the problem of maintaining a matching and propagating uncertainties on each matched primitive. Indeed, our localization method is based on the tracking of significant landmarks of the environment and integrates a multilevel uncertainty propagation stage based on the use of the Dempster-Shafer theory.

This paper deals with an absolute localization paradigm based on the cooperation of an omnidirectional vision system and a low cost panoramic range finder system. These two perception systems used independently by our laboratory provide some complementary data. This association permits us to build a robust sensorial model which integrates an important number of significant primitives. The obtained sensorial model allows one to realize an absolute localization of the mobile robot, in particular, configurations where it is not possible to determine the position with the use of one of the two sensors. First, we present our global perception system. Next, we deal with our sensorial model building approach, and finally we present an absolute-localization method which uses a Dempster-Shafer approach. We analyze the performance of our global absolute localization system on several robot's elementary moves, in an indoor environment.

In this paper we present a method of calibrating the omnidirectional sensor used in our laboratory and named SYCLOP ("Conic System for Localization and Perception"). In order to have better precision than that obtained in classical applications using this kind of sensor, we highlight the importance of calibration for the whole sensor. After this step we validate our model by a reconstruction of objects, and of an environment from two different methods, the first based on a commercial software 3DS Max/sup (R)/ and the second based on a simulator developed by us and taking parameters determined in the previous stage. The real and calculated pictures are compared, to estimate the error of reconstruction. The last part of this paper presents results obtained with the SYCLOP simulator and an experimental phase.

In this paper, an absolute localization paradigm based on the cooperation of an omnidirectional vision system composed of a conical mirror and a CCD camera and a low cost panoramic range finder system is reported. These two sensors, which have been used independently until now, provide some complementary data. This association enables us to build a robust sensorial model which integrates an important number of significant primitives. We can thus realize an absolute localization of the mobile robot in particular configurations, like symmetric environments, where it is not possible to determine the position with the use of only one of the two sensors. In a first part, we present our global perception system. In a second part, we describe our sensorial model building approach. Finally we present an absolute localization method which uses three matching criteria fused thanks to the combination rules of the Dempster-Shafer theory. The basic probability assignment got for each primitive matching enables to estimate the reliability of the localization. We test our global absolute localization system on several robots' elementary moves in an indoor and symmetric environment.

We present a method to calibrate the omnidirectional sensor used in our laboratory, named SYCLOP (conic system for localization and perception). This system, which is able to capture a panoramic image of a 2/spl pi/ radian field, consists of a CCD camera and a vertically oriented conic shaped reflector. In order to have a better precision than that obtained in classical applications using this kind of sensors, we consider the importance of calibration for the whole sensor. After having briefly recalled the theoretical framework used in hard calibration, we design the different transformations made between world object, cone reflector and pictures, as well as the different types of relationship between the world, the cone, the camera and the image coordinates. Finally, we present results obtained with the SYCLOP simulator and an experiment.

In this paper we present a method to calibrate our omnidirectional sensor named SYCLOP ("Conic System for Localization and Perception"). Actually this sensor is installing on the mobile robot SARAH, and it is able to rake a panoramic image of the environment. From this image we can localize the mobile robot using natural beacon detection. In order to have a better precision in beacons detection than that obtained in classical applications using this kind of sensors, we bring to the fore the importance of calibration for the whole sensor. We will show to the end of this paper different result obtained in real trajectory.

In this paper, we present a new language of communication between cooperative mobile robots. Generally, in order to interact they must exchange various information. In mobile robotics, communication can generally only be performed using HF channel. Originally, our language and protocol are designed to carry out safe communication between several ICAR agents (intelligent cooperative autonomous robots), but they can be used to perform another type of communication. We consider mobile robots which must be able to achieve some tasks given by another one, or a supervisor. We are developing a dynamically extendable (i.e. while robots are performing their missions) language which supports nine conversation types and works with twenty six message types. Because of communications nature (radio link), we use macro messages to reduce network congestion.