# A Low Cost Vision-Based Road-Following System for Mobile Robots
## 1 Introduction  
In recent years, autonomous mobile robots have been an active research field. Navigation is one of the significant problems that need to be addressed by robots, which work in outdoor environments [1]. Laser-based navigation has been rapidly improved and is leveraged by most commercial robots or driverless cars. A continuous curvature path is generated for car-like vehicle navigation by multiple clothoids composition and parametric adjustment [2]. The clothoids composition method for path generation relies heavily on detailed and highly accurate prior maps of the environments, which give the boundary geometric constraints. However, outside of small urban areas, it is very challenging to build, store, and transmit detailed maps since the spatial scales are so large. A novel mapless driving framework is proposed for addressing the problem of maintaining detailed maps of large areas, which combines sparse topological maps for global navigation with a laser-based perception system for local navigation [3]. However, the technical issue of laser-based navigation techniques is the reduction of the prior information because most of them require a precise 2D or 3D model of the environment. The lasers are comparative costly. In contrast, vision-based systems provide natural and powerful information of the environment at a high frame rate with a wide field of view. Image data captured by vision sensors contain rich information, such as luminance, color, texture, etc. Moreover,vision sensors are inexpensive compared to laser sensors. Thus, it could become a reliable complement of the laser-based navigation strategies. The laser-based navigation method relies strongly on accurate prior maps and is very expensive. In this paper, a low-cost, vision-based road-following system is proposed for autonomous mobile robots working in outdoor environments. Our main algorithm consists of two steps, namely, road detecting and road tracking. In the road detecting system, the road is extracted by fusing the results of a color-based road detector and a texture line detector. The UKF is adopted to track the road and the extracted following target. Then, in the road-tracking system, the road-following position in world coordinate frame is calculated by the surrounding box of the target in images. The contributions of this paper can be summarized as follows:  
1. In order to have the ability to detect the road in the majority of cases, the system combines the detection of a color-based road detector and texture line detector. Benefiting from the combination of the two detectors, the proposed road-following system is more suitable with small single lane roads, such as sidewalks, bikeways, parkways, etc. 2. The UKF is used to enhance the robustness of the system. The UKF estimates the best road borders from the measurements in occlusion or in miss detection and presents a smooth road transition frame to frame. 3. The proposed system could be easily integrated with a local controller, such as pure pursuit, model predictive control (MPC) or Douglas-Peucker (DP). This will improve the navigation ability of robots in single lane road scenarios. The rest of the paper is constructed as follows. Section 2 illustrates the related work of vision-based road detection. In Section 3, the mobile robot system is briefly introduced, including the description of the platform and system architecture. The specifications of the CCD camera used for road detection are listed in detail. Formulations of the road detection system and road tracking system are given at the end of this section. In Section 4, the road detection system is given in detail, such as the image processing module, color-based road detector and texture line detector. The UKF is used to fuse the detection results by the color-based and texture line detector and to track the target.