LIU Jian1,2, LIANG Huawei2, MEI Tao2, WANG Zhiling2, WU Yihua3, DU Mingbo1,2, DENG Yao1,2
1. Department of Automation, University of Science and Technology of China, Hefei 230027, China;
2. Institute of Applied Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230027, China;
3. Ma'anshan Power Supply Company, State Grid Anhui Electric Power Company, Ma'anshan 243000, China
Abstract：The road curb detection based on obstacle grid map is implemented mainly through the growing of road area or the seed points satisfying the features of road curb. However, these algorithms are prone to be affected by the obstacles inside the road area because of the absence of the road shape information, such as road trend information and road width distribution information. To overcome this problem, distance transformation is firstly performed on obstacle grid map to acquire the trend information, followed by the construction of width distribution histogram to express road width distribution information. Based on these, road curb seed points are extracted, the full road curb points are obtained through region growth, and the road curb shape is acquired through quadratic curve fitting. The experiment proves that the algorithm presented performs relatively more robust and accurate for the road curb extraction in relative busier road.
[1] Siegemund J, Franke U, Föerstner W. A temporal filter approach for detection and reconstruction of curbs and road surfaces based on conditional random fields[C]//IEEE Intelligent Vehicles Symposium. Piscataway, USA: IEEE, 2011: 637-642.
[2] Kellner M, Bouzouraa M E, Hofmann U. Road curb detection based on different elevation mapping techniques[C]//IEEE Intelligent Vehicles Symposium. Piscataway, USA: IEEE, 2014: 1217-1224.
[3] 高华,刘济林.车辆视频导航中道路检测方法研究进展[J].传感器与微系统,2012,31(1):1-3,6.Gao H, Liu J L. Research progress on road detection method in video navigation for vehicle[J]. Transducer and Microsystem Technology, 2012, 31(1): 1-3,6.
[4] Bar Hillel A, Lerner R, Levi D, et al. Recent progress in road and lane detection: A survey[J]. Machine Vision and Applications, 2014, 25(3): 727-745.
[5] Liu Z, Liu D X, Chen T T, et al. Curb detection using 2D range data in a campus environment[C]//Seventh International Conference on Image and Graphics. Piscataway, USA: IEEE, 2013: 291-296.
[6] Han J, Kim D, Lee M, et al. Enhanced road boundary and obstacle detection using a downward-looking LIDAR sensor[J]. IEEE Transactions on Vehicular Technology, 2012, 61(3): 971-985.
[7] Chen T T, Dai B, Liu D X, et al. Velodyne-based curb detection up to 50 meters away[C]//IEEE Intelligent Vehicles Symposium. Piscataway, USA: IEEE, 2015: 241-248.
[8] Hata A Y, Osorio F S, Wolf D F. Robust curb detection and vehicle localization in urban environments[C]//IEEE Intelligent Vehicles Symposium. Piscataway, USA: IEEE, 2014: 1257-1262.
[9] Zhao G Q, Yuan J S. Curb detection and tracking using 3D-LIDAR scanner[C]//IEEE International Conference on Image Processing. Piscataway, USA: IEEE, 2012: 437-440.
[10] Zhang Y H, Wang J, Wang X N, et al. A real-time curb detection and tracking method for UGVs by using a 3D-LIDAR sensor[C]//IEEE Conference on Control Applications. Piscataway, USA: IEEE, 2015: 1020-1025.
[11] Liu J, Liang H W, Wang Z L, et al. A framework for applying point clouds grabbed by multi-beam LIDAR in perceiving the driving environment[J]. Sensors, 2015, 15(9): 21931-21956.
