Abstract
Road detection is always the key problem of re-searches on areas of unmanned ground vehicle and computer vision. A road detection method is proposed based on online learning and multi-sensor fusion. First of all, the Lidar point clouds are projected onto the images via the joint calibration of these two kinds of sensors. Then Simple Linear Iterative Clustering is used to segment images into many superpixles. Based on that, a multilayer online learning method is proposed, in which 2 Support Vector Machines are trained to detect the road. To be specific, the superpixel layer Support Vector Machine is used to detect road roughly, and the pixel layer Support Vector Machine is then trained to classify the edge pixels of the road areas, which is classified by the upper-layer Support Vector Machine. These 2 Support Vector Machines are updated online at each frame to be adapted to the changing environment. At last, some experiments are carried out on KITTI RAW dataset and an autonomous land vehicle, and the results show the effectiveness of proposed method. The main contributions of this work lie on as follows: 1) a multilayer learning model is proposed to detect road more robustly and accurately; 2) an online learning method is proposed which can be adapted to the changing environment.
Highlights
Road detection [1], [2], [3] is one of the key technologies in multiple research areas such as unmanned ground vehicle development and machine vision
In view of the problem that the computational complexity of Support Vector Machine (SVM) classifier will raise with the increase of training samples, the online learning model proposed in this paper sets a maximum sample size and an update strategy www.ijacsa.thesai.org of positive and negative samples to ensure that the proposed method can meet the real-time requirements of road detection
In order to further verify the efficiency of multi-sensors fusion and hierarchical online learning model, we realize four kinds of road detection methods on KITTI RAW DATA: (1) Compute height difference in each super-pixel using Lidar data [25], set a threshold (25 cm) artificially
Summary
Road detection [1], [2], [3] is one of the key technologies in multiple research areas such as unmanned ground vehicle development and machine vision. With the development of 3 dimensional sensor, researchers have put forward various road detection methods based on range data. Range data of surrounding is detected from all directions based on 3D sensors [12], [13], providing adequate information of target structure without interference from illumination conditions, severe shadow, complicated texture background and etc. Taking stereoscopic vision into account such as Kinect, they are influenced by moving targets, leading to large amount of noise in detected range image [14], [15] Their observation range is more than 20 meters usually, which could not fulfil the requirements of unmanned ground vehicle environment perception. A hierarchical online learning method is put forward and verified its efficiency on KITTI raw dataset and our own unmanned ground vehicle.
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