The goal of mapping is to build a map of the environment using raw data provided by some sensors embedded on an intelligent vehicle. This map is used by an intelligent vehicle to have knowledge about its surrounding environment to better plan its future actions. In this paper, we present a method, based on occupancy grids [3], to map 3D environment. In this method, we discretize the environment in cells and the shape of each cell is approximated by one or several gaussians in order to achieve a balance between representational complexity and accuracy. Experimental results on 3D real outdoor data provided by a lidar are shown: a map of an urban environment is presented. Moreover a quantitative comparison of our method with state of the art methods is presented to show the interest of the method.

This paper addresses the issue of matching rigid and articulated shapes through probabilistic point registration. The problem is recast into a missing data framework where unknown correspondences are handled via mixture models. Adopting a maximum likelihood principle, we introduce an innovative EM-like algorithm, namely, the Expectation Conditional Maximization for Point Registration (ECMPR) algorithm. The algorithm allows the use of general covariance matrices for the mixture model components and improves over the isotropic covariance case. We analyze in detail the associated consequences in terms of estimation of the registration parameters, and propose an optimal method for estimating the rotational and translational parameters based on semidefinite positive relaxation. We extend rigid registration to articulated registration. Robustness is ensured by detecting and rejecting outliers through the addition of a uniform component to the Gaussian mixture model at hand. We provide an in-depth analysis of our method and compare it both theoretically and experimentally with other robust methods for point registration.

In this paper, we detail the hardware and software perception system designed and developed to track pedestrians using a set of offboard cameras. It has been used in the context of vulnerable safety in a car park. This architecture is divided in two parts: a fusion part to fusion the data given by the set of offboard cameras and a tracking part to sequentially estimate the position of each pedestrian present in the environment and to determine the number of pedestrians. Finally, some experimental results are presented

Building occupancy grids (OGs) in order to model the surrounding environment of a vehicle implies to fusion occupancy information provided by the different embedded sensors in the same grid. The principal difficulty comes from the fact that each can have a different resolution, but also that the resolution of some sensors varies with the location in the field of view. In this article we present a new exact approach to this issue and we explain why the problem of switching coordinate systems is an instance of the texture mapping problem in computer graphics. Therefore we introduce a calculus architecture to build occupancy grids with a graphical processor unit (GPU). Thus we present computational time results that can allow to compute occupancy grids for 50 sensors at frame rate even for a very fine grid. To validate our method, the results with GPU are compared to results obtained through the exact approach