Vivacqua Rafael, Vassallo Raquel, Martins Felipe
Federal Institute of Education, Science and Technology of Espirito Santo, Serra ES 29173-087, Brazil.
Department of Electrical Engineering, Federal University of Espirito Santo, Vitória ES 29075-910, Brazil.
Sensors (Basel). 2017 Oct 16;17(10):2359. doi: 10.3390/s17102359.
Autonomous driving in public roads requires precise localization within the range of few centimeters. Even the best current precise localization system based on the Global Navigation Satellite System (GNSS) can not always reach this level of precision, especially in an urban environment, where the signal is disturbed by surrounding buildings and artifacts. Laser range finder and stereo vision have been successfully used for obstacle detection, mapping and localization to solve the autonomous driving problem. Unfortunately, Light Detection and Ranging (LIDARs) are very expensive sensors and stereo vision requires powerful dedicated hardware to process the cameras information. In this context, this article presents a low-cost architecture of sensors and data fusion algorithm capable of autonomous driving in narrow two-way roads. Our approach exploits a combination of a short-range visual lane marking detector and a dead reckoning system to build a long and precise perception of the lane markings in the vehicle's backwards. This information is used to localize the vehicle in a map, that also contains the reference trajectory for autonomous driving. Experimental results show the successful application of the proposed system on a real autonomous driving situation.
在公共道路上实现自动驾驶需要在几厘米的范围内进行精确的定位。即使是目前基于全球导航卫星系统（GNSS）的最佳精确定位系统，也并非总能达到这种精度水平，尤其是在城市环境中，信号会受到周围建筑物和人工制品的干扰。激光测距仪和立体视觉已成功用于障碍物检测、地图绘制和定位，以解决自动驾驶问题。不幸的是，激光雷达（LIDARs）是非常昂贵的传感器，而立体视觉需要强大的专用硬件来处理相机信息。在此背景下，本文提出了一种低成本的传感器架构和数据融合算法，能够在狭窄的双向道路上实现自动驾驶。我们的方法利用了短程视觉车道标记检测器和航位推算系统的组合，以建立车辆后方车道标记的长距离精确感知。此信息用于在地图中定位车辆，该地图还包含自动驾驶的参考轨迹。实验结果表明，该系统在实际自动驾驶场景中得到了成功应用。
