# Lane Detection Algorithm Using LRF for Autonomous Navigation of Mobile Robot
## 1 Introduction
Previous studies fused the vision-based system or global positioning system (GPS) data with LRF data and used LRF as an auxiliary tool for lane recognition or built 2D maps utilizing the reflectance of LRF [17; 18; 19]. When LRF is used as an auxiliary tool, it is used for determining the position of a lane by comparing it with that recognized by a vision-based system at a measurement point or within its scope, or simply for detecting the location of a curb. In such cases, different sensors should be fused to build a system, which is inevitably expensive. When a 2D map is built using a single LRF, noises should be removed to facilitate the lane recognition, which requires the use of various complicated filters. Moreover, it cannot easily discern obstacles [20; 21; 22; 23; 24; 25; 26]. On the other hand, when a 3D map is built using a single LRF, the criteria for lane recognition becomes clear and the obstacles can be detected more easily. Noise removal for lane recognition becomes easier as well by simply deleting the parts other than the lane-forming height. In this study, road conditions were scanned using a single LRF, and a 3D road map was built. Further, lanes were extracted from the map built by means of the feature point extraction algorithm so that they could be recognized. Typically, road lanes are divided by continuous lines and dashed lines. Continuous lines can be easily recognized and tracked with a 3D road map and the feature point extraction algorithm. However, when these lines become faded or broken over time, lane recognition becomes difficult and accidents are more likely to occur. To prevent such accidents, we present in this paper a lane prediction method using a curvature algorithm. This algorithm has the advantages of simple calculation and excellent expression of simple curves. It is, therefore, suitable for rapid processing. The proposed method was designed to enable a lane tracking system to track a discontinuous lane by predicting the path of the lane lying ahead based on the lane tendencies figured out from those recognized earlier and by applying the curvature theory. In the case of dashed lines, precise path prediction was implemented by updating the coordinates of the dashed-line lanes along the path predicted with the curvature algorithm. This method can be effective not only in reducing the real-time system load compared to the lane prediction by means of a complicated prediction algorithm, but also in allocating system resources. The rest of this paper is organized as follows: Section 2 explains the structure and modeling of the system used in this study. Section 3 explains the algorithm with which a 3D road map is built with 2D data and the method for extracting feature points. Section 4 explains how lane tendencies can be determined for the prediction of a discontinuous lane and the prediction method using the curvature algorithm. Section 5 explains a stable tracking algorithm for implementing tracking by a mobile robot by recognizing lane feature points acquired from the 3D road map. Finally, in Section 6, the experimental results and conclusions of this study are presented.