Tiberiu Marita

3D lane detection system based on stereovision

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 by the vertical and horizontal clothoid curves, the lane width and the roll angle. The lane detection is integrated into a tracking process. The current lane parameters are predicted using the past parameters and the vehicle dynamics, and this prediction provides search regions for the current detection. The detection starts with estimation of the vertical profile, using the stereo-provided 3D information, and afterwards the horizontal profile is detected using a model-matching technique in the image space, using the knowledge of the already detected vertical profile. The roll angle is detected last, by estimating the difference of the average heights of the left and right lane borders. The detection results are used to update the lane state through Kalman filtering.

High accuracy stereo vision system for far distance obstacle detection

This paper presents a high accuracy stereo vision system for obstacle detection and vehicle environment perception in a large variety of traffic scenarios, from highway to urban. The system detects obstacles of all types, even at high distance, outputting them as a list of cuboids having a position in 3D coordinates, size and speed.

A Sensor for Urban Driving Assistance Systems Based on Dense Stereovision

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 urban scenario through a multitude of detection modules, built on top of a hybrid (hardware plus software) dense stereo reconstruction engine. The sensor is able to detect and track clothoid and non-clothoid lanes, cars, pedestrians (classified as such), and drivable areas in the absence of lane markings. The hybrid stereovision engine and the proposed detection algorithms allow accurate sensing of the demanding urban scenario at a high frame rate.

Camera Calibration Method for Far Range Stereovision Sensors Used in Vehicles

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 relative camera angles can lead to disastrous consequences in the whole stereo-vision process: incorrect epipolar lines and consequently lack of reconstructed 3D points. Therefore we propose an off-line method able to give a very accurate estimation of these parameters and on-line methods for monitoring their stability in time taking into account the real-world automotive conditions, which can introduce drifts to the initial parameters due vibrations, temperature variations etc

Accurate Ego-Vehicle Global Localization at Intersections Through Alignment of Visual Data With Digital Map

This paper proposes a method for achieving improved ego-vehicle global localization with respect to an approaching intersection, which is based on the alignment of visual landmarks perceived by the on-board visual system, with the information from a proposed extended digital map (EDM). The visual system relies on a stereovision system that provides a detailed 3-D description of the environment, including road landmark information (lateral lane delimiters, painted traffic signs, curbs, and stop lines) and dynamic environment information (other vehicles). An EDM is proposed, which enriches the standard map information with a detailed description of the intersection required for current lane identification, landmark alignment, and ego-vehicle accurate global localization. A novel approach for lane-delimiter classification, which is necessary for the lane identification, is also presented. An original solution for identifying the current lane, combining visual and map information with the help of a Bayesian network (BN), is proposed. Extensive experiments have been performed, and the results are evaluated with a Global Navigation Satellite System of high accuracy (2 cm). The achieved global localization accuracy is of submeter level, depending on the performance of the stereovision system.

Driving environment perception using stereovision

This paper presents a high accuracy, far range stereovision approach for driving environment perception based on 3D lane and obstacle detection. Stereovision allows the elimination of the common assumptions used in most monocular systems: flat road, constant pitch angle or absence of roll angle. The lane detection method is based on clothoidal 3D lane model. The detected lane parameters are the vertical and horizontal curvatures, the lane width and the roll angle. The detected lane profile is used for road obstacle features separation. Based on a vicinity criteria the over road 3D points are grouped and tracked over frames. The system detects and classifies the meaningful obstacles in terms of 3D position, size and speed.

Online Extrinsic Parameters Calibration for Stereovision Systems Used in Far-Range Detection Vehicle Applications

This paper presents a high-accuracy online calibration method for the absolute extrinsic parameters of a stereovision system that is suited for far-distance, vision-based vehicle applications. The method uses as prior knowledge the intrinsic parameters and the relative extrinsic parameters (relative position and orientation) of the two cameras, which are calibrated using offline procedures. These parameters remain unchanged if the two cameras are mounted on a rigid frame (stereo rig). The absolute extrinsic parameters define the position and orientation of the stereo system relative to a world coordinate system. They must be calibrated every time after mounting the stereo rig in the vehicle and are subject to changes due to static load factors for the used car setup. The proposed method is able to perform online the estimation of the absolute extrinsic parameters by driving the car on a flat and straight road, parallel with the longitudinal lane markers. The edge points of the longitudinal lane markers are extracted after a 2-D image classification process and reconstructed by stereovision in the stereo-rig coordinate system. After filtering out the noisy 3-D points, the normal vectors of the world coordinate system axes are estimated in the stereo-rig coordinate system by 3-D data fitting. The output of the method is the height and the orientation of the stereo cameras that are relative to the world coordinate system.

Stereovision-Based Sensor for Intersection Assistance

The intersection scenario imposes radical changes in the physical setup and in the processing algorithms of a stereo sensor. Due to the need for a wider field of view, which comes with distortions and reduced depth accuracy, increased accuracy in calibration and dense stereo reconstruction is required. The stereo matching process has to be performed on rectified images, by a dedicated stereo board, to free processor time for the high-level algorithms. In order to cope with the complex nature of the intersection, the proposed solution perceives the environment in two modes: a structured approach, for the scenarios where the road geometry is estimated from lane delimiters, and an unstructured approach, where the road geometry is estimated from elevation maps. The structured mode provides the parameters of the lane, and the position, size, speed and class of the static and dynamic objects, while the unstructured mode provides an occupancy grid having the cells labeled as free space, obstacle areas, curbs and isles.

Stop-line detection and localization method for intersection scenarios

This paper presents a method for the detection and localization of stop-lines and other horizontal road markings as bicycle or pedestrian crossings encountered in road environments. The few existing stop-line detection approaches found in literature are based on monocular vision, and therefore cannot infer high accuracy information from the visual data. The proposed solution uses a hybrid approach that combines 2D detections in grayscale images with stereo-vision based 3D validations in order to increase the robustness and accuracy of the results. Model based reasoning is also used to eliminate false positive situations and to detect exactly the objects of interest (stop-lines, pedestrian or bicycle crossings). The detected horizontal road markings can be used as landmarks for a very accurate longitudinal localization of the ego vehicle within intersection scenarios compensating missing GPS data or its lack of accuracy.

Development of an omnidirectional stereo vision system

Although stereo systems built up of omnidirectional cameras offer the possibility of providing information for a 360 grade field of view, the research in the field is still in an early stage compared to perspective systems. We provide in this study an extensive overview on existing techniques for camera calibration, stereo rectification, stereo matching methods and 3D depth reconstruction on omnidirectional images. A comparison of methods, together with a few ideas of improvement is presented, establishing an equivalency, and transformation between an omnidirectional and a perspective vision system.