Florin Oniga

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Modeling and tracking the driving environment is a complex problem due to the heterogeneous nature of the real world. In many situations, modeling the obstacles and the driving surfaces can be achieved by the use of geometrical objects, and tracking becomes the problem of estimating the parameters of these objects. In the more complex cases, the scene can(More)
A new approach for the detection of the road surface and obstacles is presented. The 3D data from dense stereo is transformed into a rectangular elevation map. A quadratic road surface model is first fitted, by a RANSAC approach, to the region in front of the ego vehicle. This primary solution is then refined by a region growing-like process, driven by the(More)
This work presents a 3D lane detection method based on stereovision. The stereovision algorithm allows the elimination of the common assumptions: flat road, constant pitch angle or absence of roll angle. Moreover, the availability of 3D information allows the separation between the road and the obstacle features. The lane is modeled as a 3D surface, defined(More)
The urban driving environment is a complex and demanding one, requiring increasingly complex sensors for the driving assistance systems. These sensors must be able to analyze the complex scene and extract all the relevant information, while keeping the response time as low as possible. The sensor presented in this paper answers to the requirements of the(More)
This paper will present an obstacle detection system that that relies on the 3D information provided by stereo reconstruction. The 3D features must be separated in road features and obstacle features. Instead of relying on the flatness of the road, the vertical road profile is modeled as a clothoid, and is estimated from the lateral projection of the 3D(More)
A new approach for the stereovision problem is presented, aiming to increase the accuracy of stereo reconstruction. The proposed method is edge-based and consists of the correlation of left and right contours, detected with sub-pixel accuracy. The steps of the stereo matching process are: segmentation of each contour into basic contours (strongly- and(More)
This paper presents a camera calibration method for far-range stereo-vision used for driving environment perception on highways. For a high accuracy stereovision system the most critical camera parameters are the relative extrinsic parameters which are describing the geometry of the stereo-rig. Experiments proved that even a few seconds drift of the(More)
An approach for the detection of straight and curved curbs (border of relevant traffic isles, sidewalks, etc) is presented, in the context of urban driving assistance systems. A rectangular elevation map is built from 3D dense stereo data. Edge detection is applied to the elevation map in order to highlight height variations. We propose a method to reduce(More)
In this paper we present a real-time algorithm that detects curbs using a cubic spline model. A Digital Elevation Map (DEM) is used to represent the dense stereovision data. Curb measurements (cells) are detected on the current frame DEM. In order to compensate the small number of curb measurements for each frame we perform temporal integration. The result(More)
A real-time algorithm for curb detection in traffic scenes, based on dense stereovision, is proposed. Curbs are modeled as cubic polynomial curves. 3D points from stereovision are transformed into a Digital Elevation Map (DEM), in order to have a compact representation of the 3D space. Curb points are detected as the cells of the DEM that present a specific(More)