In this paper, we deal with an original advanced driver assistance system (ADAS) based on the use of omnidirectional vision and an evidential fusion architecture. The panoramic perception solution permits us to address efficiently the problem of close vehicles detection but also the monitoring side traffic system. The fusion and integration of this sensorial data stream is assumed by a credibilist architecture based on the transferable belief model (TBM) of SMETS. This paradigm permits the filtering of false alarms efficiently by an optimal management of the uncertainties estimation.

Firstly this article presents a multi-level architecture permitting the localization of a mobile platform and secondly an incremental construction of the environment's map. The environment will be modeled by an occupancy grid built with information provided by the stereovision system situated on the platform. The reliability of these data is introduced to the grid by the propagation of uncertainties managed thanks to the theory of the Transferable Belief Model.

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 the localization and map building paradigm in an unknown indoor environment. We propose an exploration method based on the use of the sensorial data provided by an omnidirectional stereoscopic vision system. First, we link the problem of sensorial model construction with two omnidirectional images. We propose an approach based on the fusion of several criteria, which is realized according to Dempster-Shafer rules. We then deal with the matching problem of the stereo sensorial model with an environment map integrating all the previous primitive observations. We propose two matching approaches based on different selection criteria: the Hausdorff distance and the cumulated Cartesian distance. We also present our incremental map building paradigm based on the hypothesis of a non a priori knowledge. Finally, we deal with the problem of allowing a robot to localize itself and to construct concurrently a representation of its environment.

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, 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.

Presents a system of absolute localization based on the stereoscopic omnidirectional vision. To do it we use an original perception system which allows our omnidirectional vision sensor SYCLOP to move along a rail. The first part of our study deals with the problem of building the sensorial model with the help of the two stereoscopic omnidirectional images. To solve this problem we propose an approach based on the fusion of several criteria which will be made according to Dempster-Shafer rules. The second part is devoted to exploiting this sensorial model to localize the robot thanks to matching the sensorial primitives with the environment map. We analyze the performance of our global absolute localization system on several robot elementary moves, in different environments.

Presents results for state estimation based on noisy observations suffering from two different types of uncertainties. The first uncertainty is a stochastic process with given statistics. The second uncertainty is only known to be bounded, the exact underlying statistics are unknown. State estimation tasks of this kind typically arise in target localization, navigation, and sensor data fusion. A new estimator has been developed, that combines set theoretic and stochastic estimation in a rigorous manner. The estimator is efficient and, hence, well-suited for practical applications. It provides a continuous transition between the two classical estimation, concepts, because it converges to a set theoretic estimator, when the stochastic error goes to zero, and to a Kalman filter, when the bounded error vanishes. In the mixed noise case, the new estimator provides solution sets that are uncertain in a statistical sense.

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.

An omnidirectional range sensor is reported. This active vision sensor combines a CCD camera and a laser diode. We use two methods to obtain the depth of the scene: a calibration method and a least square method. We describe the prototype we made. Experimental results are presented. A comparative test shows that this sensor seems to be as accurate as a laser telemeter but less sensitive to nonalignment. Its results are better than an ultrasonic sensor. Finally, we compare three segmentation algorithms and their results on the set of points given by the sensor.