International audience

International audience

International audience

International audience

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.

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.

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.

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.

This paper presents a system of absolute localization based on the cooperation of a stereoscopic omnidirectional vision system and a dead-reckoning system. To do it we use an original perception system which allows our omnidirectional vision sensor SYCLOP to move along a rail. We address 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. We then exploit this sensorial model to localize the robot thanks to matching the sensorial primitives with the environment map. We use the dead-reckoning prediction to decrease the combinatory aspect of the matching algorithm. We analyze the performance of our global absolute localization system on several robots' elementary moves, in an indoor environment.