Road curb detection based on different elevation mapping techniques

A road curb detection algorithm for a 3D sensor, e.g. a dense stereo camera, is presented in this paper. The road curb detection is based on a digital elevation map. Different techniques and coordinate systems for mapping the height values are compared theoretically and by simulating a different quality of ego motion data. Furthermore we introduce a new approach of finding road curbs in an elevation map, which is based on a calculation of the most probable path. Using an elevation map the curb height can be calculated in an additional step. For evaluation we use highly accurate reference sensors and compare the detected curbs to a ground truth. Additionally we introduce a novel criteria to describe the quality of an elevation map and discuss the results. The road detection algorithm works in real-time and has a position accuracy of about 10 cm and an height error of about 1.5 cm.

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

[2]  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.

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

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

[5]  S. Nedevschi,et al.  Curb segments detection with temporal filtering for urban driving scenarios , 2008, 2008 4th International Conference on Intelligent Computer Communication and Processing.

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

[7]  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.

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

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

[10]  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.

[11]  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).

[12]  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).

[13]  Alan C. Schultz,et al.  Representing a 3-D environment with a 2 1/2 -D map structure , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[14]  Richard M. Murray,et al.  System architectures and environment modeling for high-speed autonomous navigation , 2006 .

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