Multi-cue, Model-Based Detection and Mapping of Road Curb Features Using Stereo Vision

In this paper a new approach for road curb detection is presented, which is able to detect road curbs independently of their geometry and orientation in relation to the car. This approach includes a novel algorithm for curb feature detection, which combines cues for road curbs from a 3D point cloud and an intensity image, both gained from a stereo camera system. The edge detection operators used for the detection of curb features are adapted according to curb models. With these adaptive operators road curbs can be found independently from their position and orientation in the image and the correct curb height can be calculated. Further we introduce a computation efficient grid map, which is based on a local linear description of the road curb in every cell and is able to represent the road curbs three-dimensionally. A chaining mechanism, which benefits from the estimated curb directions in the grid map, generates road curbs represented as 3D polygonal chains. An evaluation of the detected curbs using DGPS measurements of the curbs and the car shows a high position and height accuracy for this real time capable approach.

[1]  R. Aufrere,et al.  Multiple sensor fusion for detecting location of curbs, walls, and barriers , 2003, IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No.03TH8683).

[2]  Raúl Rojas,et al.  Camera based detection and classification of soft shoulders, curbs and guardrails , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[3]  Ulrich Hofmann,et al.  An interval based representation of occupancy information for driver assistance systems , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[4]  Sergiu Nedevschi,et al.  Processing Dense Stereo Data Using Elevation Maps: Road Surface, Traffic Isle, and Obstacle Detection , 2010, IEEE Transactions on Vehicular Technology.

[5]  David Fernández Llorca,et al.  Road curb and lanes detection for autonomous driving on urban scenarios , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[6]  Ulrich Hofmann,et al.  Road curb detection based on different elevation mapping techniques , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[7]  Paul E. Rybski,et al.  Fast feature detection and stochastic parameter estimation of road shape using multiple LIDAR , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[8]  Jannik Fritsch,et al.  A self-adaptive approach for curbstone/roadside detection based on human-like signal processing and multi-sensor fusion , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[9]  Roberto Manduchi,et al.  Robust curb and ramp detection for safe parking using the Canesta TOF camera , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[10]  Klaus Diepold,et al.  EgoMaster: A central ego motion estimation for driver assist systems , 2009, 2009 IEEE International Conference on Control and Automation.

[11]  Ulrich Hofmann,et al.  360 Degree multi sensor fusion for static and dynamic obstacles , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[12]  Markus Enzweiler,et al.  Towards multi-cue urban curb recognition , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[13]  Wolfgang Förstner,et al.  A temporal filter approach for detection and reconstruction of curbs and road surfaces based on Conditional Random Fields , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[14]  Mohamed Essayed Bouzouraa.  Belegungskartenbasierte Umfeldwahrnehmung in Kombination mit objektbasierten Ansätzen für Fahrerassistenzsysteme , 2012 .

[15]  W. Sardha Wijesoma,et al.  Road-boundary detection and tracking using ladar sensing , 2004, IEEE Transactions on Robotics and Automation.

[16]  Martin Kellner,et al.  Laserscanner based road curb feature detection and efficient mapping using local curb descriptions , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[17]  Fernando Santos Osório,et al.  Robust curb detection and vehicle localization in urban environments , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[18]  Roland Siegwart,et al.  Curb detection for a pedestrian robot in urban environments , 2012, 2012 IEEE International Conference on Robotics and Automation.

[19]  Roberto Manduchi,et al.  Detection and Localization of Curbs and Stairways Using Stereo Vision , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[20]  Sergiu Nedevschi,et al.  Curb detection for driving assistance systems: A cubic spline-based approach , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).