An Illumination-Invariant Nonparametric Model for Urban Road Detection

作     者：Su, Yingna Gao, Yicheng Zhang, Yigong Alvarez, Jose M. Yang, Jian Kong, Hui 

作者机构：Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China Canberra Res Lab NICTA Canberra ACT 2600 Australia 

出 版 物：《IEEE TRANSACTIONS ON INTELLIGENT VEHICLES》 (IEEE Trans. Intell. Veh.)

年 卷 期：2019年第4卷第1期

页      面：14-23页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0823[工学-交通运输工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Jiangsu Province Natural Science Foundation [BK20151491] Natural Science Foundation of China 

主　　题：Road detection vanishing point Dijkstra algorithm minimum-cost model single-line LIDAR sensor 

摘      要：In this paper, we propose an illumination-invariant nonparametric model for urban road detection based on a monocular camera and a single-line LIDAR sensor. With the monocular camera, we propose a new shadow removal method to obtain an illumination-invariant image representation. Consequently, we can accurately locate the road vanishing point after removing the adverse shadowy effect. With the constraint of the detected vanishing point, we propose a Dijkstra-based method to compute a minimum-cost map, where the minimum-cost path from the vanishing point to any other pixel can be found. With the single line LIDAR sensor, we can locate a few potential curb points in the image bottom region, and thus we can obtain several corresponding minimum-cost paths that originate from the vanishing point to the curb points. Thereafter, two most likely road borders can be found from these paths, respectively. Our learning-free method has been tested on over 4000 images of the KITTI-Odometry Dataset [A. Geiger, P. Lenz, and ***, Are we ready for autonomous driving? The KITTI vision benchmark suite, in Proc. IEEE Conf. Comput. Vision Pattern Recognit., 2012, pp. 3354-3361.] and the Oxford Robotcar Dataset [W. Maddern, G. Pascoe, C. Linegar, and P. Newman, 1 year, 1000 km: The Oxford robotcar dataset, Int. J. Robot. Res., vol. 36, no. 1, pp. 3-15, 2017.]. It works accurately on a variety of road scenes and is competitive compared to state-of-the-art deep learning methods that need extensive training data.