Road curb and intersection detection using A 2D LMS

In most urban roads, and similar environments such as in theme parks, campus sites, industrial estates, science parks and the like, the painted lane markings that exist may not be easily discernible by CCD cameras due to poor lighting, bad weather conditions, and inadequate maintenance. An important feature of roads in such environments is the existence of pavements or curbs on either side defining the road boundaries. These curbs, which are mostly parallel to the road, can be hardnessed to extract useful features of the road for implementing autonomous navigation or driver assistance systems. However, extraction of the curb or road edge feature using vision image data is a very formidable task as the curb is not conspicuous in the vision image. To extract the curb using vision data requires extensive image processing, heuristics and very favorable ambient lighting. In our approach, road curbs are extracted speedily using range data provided by a 2D Laser range Measurement System (LMS). Experimental results are presented to demonstrate the viability, and effectiveness, of the proposed methodology and its robustness to different road configurations including road intersections

Laser-camera composite sensing for road detection and tracing

An important feature in most urban roads and similar environments, such as in theme parks, campus sites, industrial estates, science parks, and the like, is the existence of pavements or curbs on either side de?ning the road boundaries. These curbs, which are mostly parallel to the road, can be harnessed to extract useful features of the road for implementing autonomous navigation or driver assistance systems. However, vision-alone methods for extraction of such curbs or road edge features with accurate depth information is a formidable task, as the curb is not conspicuous in the vision image and also requires the use of stereo images. Further, bad lighting, adverse weather conditions, nonlinear lens aberrations, or lens glare due to sun and other bright light sources can severely impair the road image quality and thus the operation of vision-alone methods. In this paper an alternative and novel approach involving the fusion of 2D laser range and monochrome vision image data is proposed to improve the robustness and reliability. Experimental results are presented to demonstrate the viability and effectiveness of the proposed methodology and its robustness to different road configurations and shadows

Vehicle localization using landmarks obtained by a lidar mobile mapping system

Accurate and reliable localization in extensive outdoor environments will be a key ability of future driver assistance systems and autonomously driving vehicles. Relative localization, using sensors and a pre-mapped environment, will play a crucial role for such systems, because standard global navigation satellite system (GNSS) solutions will not be able to provide the required reliability. However, it is obvious that the environment maps will have to be quite detailed, making it a must to produce them fully automatically. In this paper, a relative localization approach is evaluated for an environment of substantial extent. The pre-mapped environment is obtained using a LIDAR mobile mapping van. From the raw data, landmarks are extracted fully automatically and inserted into a landmark map. Then, in a second campaign, a robotic vehicle is used to traverse the same scene. Landmarks are extracted from the sensor data of this vehicle as well. Using associated landmark pairs and an estimation approach, the positions of the robotic vehicle are obtained. The number of matches and the matching errors are analyzed, and it is shown that localization based on landmarks outperforms the vehicle's standard GNSS solution

A Synergistic Approach for Recovering Occlusion-Free Textured 3D Maps of Urban Facades from Heterogeneous Cartographic Data

In this paper we present a practical approach for generating an occlusion-free textured 3D map of urban facades by the synergistic use of terrestrial images, 3D point clouds and area-based information. Particularly in dense urban environments, the high presence of urban objects in front of the facades causes significant difficulties for several stages in computational building modeling. Major challenges lie on the one hand in extracting complete 3D facade quadrilateral delimitations and on the other hand in generating occlusion-free facade textures. For these reasons, we describe a straightforward approach for completing and recovering facade geometry and textures by exploiting the data complementarity of terrestrial multi-source imagery and area-based information

Evaluation of automatically extracted landmarks for future driver assistance systems

In the future, vehicles will gather more and more spatial information about their environment, using on-board sensors such as cameras and laser scanners. Using this data, e.g. for localization, requires highly accurate maps with a higher level of detail than provided by today's maps. Producing those maps can only be realized economically if the information is obtained fully automatically. It is our goal to investigate the creation of intermediate level maps containing geo-referenced landmarks, which are suitable for the specific purpose of localization. To evaluate this approach, we acquired a dense laser scan of a 22 km scene, using a mobile mapping system. From this scan, we automatically extracted pole-like structures, such as street and traffic lights, which form our pole database. To assess the accuracy, ground truth was obtained for a selected inner-city junction by a terrestrial survey. In order to evaluate the usefulness of this database for localization purposes, we obtained a second scan, using a robotic vehicle equipped with an automotive-grade laser scanner. We extracted poles from this scan as well and employed a local pole matching algorithm to improve the vehicle's position

Road curb tracking in an urban environment

Road detection and tracking is very useful in the synthesis of driver assistance and intelligent transportation systems. In this paper a methodology is proposed based on the extended Kalman filer for robust road curb detection and tracking using a combination of onboard active and passive sensors. The problem is formulated as detecting and tracking a maneuvering target in clutter using onboard sensors on a moving platform. The primary sensors utilized are a 2 dimensional SICK laser scanner, five encoders and a gyroscope, together with an image sensor (CCD camera). Compared to the active 20 laser scanner the CCD camera is capable of providing observations over an extended horizon, thus making available much useful information about the curb trend, which is exploited in mainly the laser based tracking algorithm. The advantage of the proposed image enhanced laser detection/tracking method, over laser alone detection/tracking, is illustrated using simulations and its robustness to varied road curvatures, branching, turns and scenarios, is demonstrated through experimental results. © 2003 ISlF

Trajectory generation for lane-change maneuver of autonomous vehicles

Lane-change maneuver is one of the most thoroughly investigated automatic driving operations that can be used by an autonomous self-driving vehicle as a primitive for performing more complex operations like merging, entering/exiting highways or overtaking another vehicle. This thesis focuses on two coherent problems that are associated with the trajectory generation for lane-change maneuvers of autonomous vehicles in a highway scenario: (i) an effective velocity estimation of neighboring vehicles under different road scenarios involving linear and curvilinear motion of the vehicles, and (ii) trajectory generation based on the estimated velocities of neighboring vehicles for safe operation of self-driving cars during lane-change maneuvers. ^ We first propose a two-stage, interactive-multiple-model-based estimator to perform multi-target tracking of neighboring vehicles in a lane-changing scenario. The first stage deals with an adaptive window based turn-rate estimation for tracking maneuvering target vehicles using Kalman filter. In the second stage, variable-structure models with updated estimated turn-rate are utilized to perform data association followed by velocity estimation. Based on the estimated velocities of neighboring vehicles, piecewise Bezier-curve-based methods that minimize the safety/collision risk involved and maximize the comfort ride have been developed for the generation of desired trajectory for lane-change maneuvers. The proposed velocity-estimation and trajectory-generation algorithms have been validated experimentally using Pioneer3- DX mobile robots in a simulated lane-change environment as well as validated by computer simulations

Automated Classification of Airborne Laser Scanning Point Clouds

Making sense of the physical world has always been at the core of mapping. Up until recently, this has always dependent on using the human eye. Using airborne lasers, it has become possible to quickly "see" more of the world in many more dimensions. The resulting enormous point clouds serve as data sources for applications far beyond the original mapping purposes ranging from flooding protection and forestry to threat mitigation. In order to process these large quantities of data, novel methods are required. In this contribution, we develop models to automatically classify ground cover and soil types. Using the logic of machine learning, we critically review the advantages of supervised and unsupervised methods. Focusing on decision trees, we improve accuracy by including beam vector components and using a genetic algorithm. We find that our approach delivers consistently high quality classifications, surpassing classical methods