Improving localization accuracy for autonomous driving in snow-rain environments
Improving localization accuracy for autonomous driving in snow-rain environments
复制标题
提高雨雪环境自动驾驶定位精度
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Keisuke Yoneda
中科院分区：
文献类型：
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作者：
Mohammad Aldibaja;Noaki Suganuma;Keisuke Yoneda
精确定位是自动驾驶汽车最重要的问题之一。提出了一种新的基于LIDAR的自动驾驶汽车定位方法，以提高自动驾驶汽车在雨雪环境下的定位性能。主成分分析(PCA)用于重建LIDAR图像，以提高图像质量并使像素值与地图图像中的像素值保持一致。此外，结合边缘轮廓匹配，在减少车道内雪线影响方面提高了横向控制的精度。实际实验结果验证了该方法的可靠性，为最高时速60公里的雪地自主驾驶提供了可接受的定位误差。
Accurate localization is one of the most important issues for autonomous cars. This paper suggests a new LIDAR based method for improving the performance of localizing autonomous cars especially in snow-rain environment. Principal Component Analysis (PCA) is used to reconstruct LIDAR images in terms of enhancing quality and aligning pixel values to those in map images. In addition, edge-profile matching is incorporated to increase the accuracy of the lateral controlling in terms of reducing the effects of snow lines inside lanes. The real experimental results have verified that the proposed method is reliable and provides an acceptable localization error for driving autonomously in snow environments at maximum speed of 60 Km/h.