# A Low Cost Vision-Based Road-Following System for Mobile Robots
## 2 Related Work  
The vision sensors are defined as passive sensors and can be used for some specific applications, such as road detection, traffic sign recognition and obstacle identification [4]. Road detection is a fundamental issue in field robot navigation systems, which have attracted keen attention in the past several decades. As a result, various navigation algorithms for different types of vision sensors have been proposed [5; 6; 7; 8]. Some of the works employ a monocular camera, stereo/multi camera, or depth camera systems for navigation. The road detection and target extraction are essential for vision-based navigation in outdoor environments. For these reasons, many state-of-the-art field robot systems employ vision sensors for road detection. [PERSON] et al. presented a mobile robot using a vision system to navigate in an unstructured environment [9]. The vision system consisted of two cameras. One is used for road region detection, and the other is used for road direction estimation. For a system trained by driving a robot through its environment, a vision-based road detection allows us to classify each individual image pixel as either an obstacle or the ground based on its color appearance [10]. For robot navigation in agricultural environments or hazardous related areas, a method for extracting and tracking man-made roads segments color images in small areas was proposed. These small areas are characterized later by color and texture attributes, and features are classified using the KNN rule or the Support Vector Machines method [4]. A vision-based road detection method was proposed to realize visual guiding navigation for autonomous land vehicles [11]. In this case, the images are segmented into road and non-road region using Otsu thresholding algorithm. Some methods detect road regions on triangular [12; 13; 14] or trapezoidal shape [15; 16] and linear boundary constraints [17]. A triangle constraint means that the road surface is triangular in the image. In [12], one vanishing point and two road boundaries are detected to form a triangular road region. In [13], a self-supervised method is proposed, where positive and negative samples are initially extracted from the inside and outside of the triangle that corresponds to the road surface in the image. The road boundaries are fit to a trapezoidal model described by 5 parameters [16]. A number of deep learning-based methods for road surface segmentation have been proposed in recent years. A multitask deep convolutional network is developed for the problem of lane detection, which simultaneously detects the presence of the target and its geometric attributes with respect to the region of interest (ROI) [18]. Inspired by an iterative deep analysis thinking, a novel method is proposed to solve the optimal precision by balancing local and global information to conduct pixel-level classification for road segmentation [19]. A deep convolutional encoder-decoder architecture is proposed for semantic segmentation, termed SegNet [20]. SegNet performs competitively, achieving high scores for road scene understanding based on large and well-known datasets. However, end-to-end learning of deep segmentation architectures remains a challenge. In the literature ([15]), a symmetric and fixed-height trapezoid is used to reduce model parameters, and a single Gaussian model is used to describe the road surface. [PERSON] et al. proposed an online structural learning method, and the road boundary fitting is adopted to detect a more reliable boundary, while straight roads are assumed [17]. A vision-based control law is proposed to follow the road boundary with a monocular camera. This method is a part of the topological navigation to reduce prior information and enhance scalability of the map [21]. Although these methods seem to produce satisfactory results for pixel-wise scene segmentation, they require additional post-processing to determine the position of road marker images.