An Illumination-Invariant Nonparametric Model for Urban Road Detection

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 R.Urtasun, “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.