Robust Intensity-Based Localization Method for Autonomous Driving on Snow-Wet Road Surface
Robust Intensity-Based Localization Method for Autonomous Driving on Snow-Wet Road Surface
复制标题
DOI：
10.1109/tii.2017.2713836
复制
发表时间：
2017-10-01
影响因子：
12.3
通讯作者：
Yoneda, Keisuke
中科院分区：
文献类型：
--
作者：
Aldibaja, Mohammad;Suganuma, Naoki;Yoneda, Keisuke
自动驾驶汽车近年来发展迅速。在前期实施阶段，必须解决许多特殊问题，才能将这项技术推向市场。本文主要研究在雪和湿路面环境下的驾驶问题。首先，在潮湿的路面上，激光成像检测和测距（LIDAR）反射率的质量会下降。因此，设计了一种累积策略来增加在线LIDAR图像的密度。为了增强累积图像的纹理，使用主成分分析来理解地图图像中的几何结构和纹理模式。然后，使用相对于由每个特征向量占的方差分布的领先主成分来重建LIDAR图像。其次，雪线的出现使LIDAR图像中的预期道路背景变形。因此，提取LIDAR和地图图像的边缘轮廓以编码车道线和路边边缘。然后计算两个轮廓之间的边缘匹配以改善横向方向上的定位。所提出的方法已经使用2016-2017年冬季在日本Suzu和金泽收集的真实的数据进行了测试和评估。实验结果表明，该方法提高了自动驾驶在湿路面上的鲁棒性，在雪线存在的情况下提供了稳定的车辆横向定位性能，并在60 km/h的速度下显着降低了整体定位误差。
Autonomous vehicles are being developed rapidly in recent years. In advance implementation stages, many particular problems must be solved to bring this technology into the market place. This paper focuses on the problem of driving in snow and wet road surface environments. First, the quality of laser imaging detection and ranging (LIDAR) reflectivity decreases on wet road surfaces. Therefore, an accumulation strategy is designed to increase the density of online LIDAR images. In order to enhance the texture of the accumulated images, principal component analysis is used to understand the geometrical structures and texture patterns in the map images. The LIDAR images are then reconstructed using the leading principal components with respect to the variance distribution accounted by each eigenvector. Second, the appearance of snow lines deforms the expected road context in LIDAR images. Accordingly, the edge profiles of the LIDAR and map images are extracted to encode the lane lines and roadside edges. Edge matching between the two profiles is then calculated to improve localization in the lateral direction. The proposed method has been tested and evaluated using real data that are collected during the winter of 2016-2017 in Suzu and Kanazawa, Japan. The experimental results show that the proposed method increases the robustness of autonomous driving on wet road surfaces, provides a stable performance in laterally localizing the vehicle in the presence of snow lines, and significantly reduces the overall localization error at a speed of 60 km/h.