LiDAR-Based Localization on Highways Using Raw Data and Pole-Like Object Features

Sheng-Cheng Lee, Victor Lu, Chieh-Chih Wang, Wen-Chieh Lin
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Abstract

Poles on highways provide important cues for how a scan should be localized onto a map. However existing point cloud scan matching algorithms do not fully leverage such cues, leading to suboptimal matching accuracy in highway environments. To improve the ability to match in such scenarios, we include pole-like objects for lateral information and add this information to the current matching algorithm. First, we classify the points from the LiDAR sensor using the Random Forests classifier to find the points that represent poles. Each detected pole point will then generate a residual by the distance to the nearest pole in map. The pole residuals are later optimized along with the point-to-distribution residuals proposed in the normal distributions transform (NDT) using a nonlinear least squares optimization to get the localization result. Compared to the baseline (NDT), our proposed method obtains a 34% improvement in accuracy on highway scenes in the localization problem. In addition, our experiment shows that the convergence area is significantly enlarged, increasing the usability of the self-driving car localization algorithm on highway scenarios.
基于激光雷达的高速公路原始数据和极状物体特征定位
高速公路上的电线杆为如何将扫描定位到地图上提供了重要线索。然而,现有的点云扫描匹配算法并没有充分利用这些线索,导致高速公路环境下的匹配精度不理想。为了提高在这种情况下的匹配能力,我们在横向信息中加入了类似极点的对象,并将这些信息添加到当前的匹配算法中。首先,我们使用随机森林分类器对LiDAR传感器中的点进行分类,以找到代表极点的点。然后,每个检测到的极点点将根据到最近极点的距离产生残差。然后将极点残差与正态分布变换(NDT)中提出的点到分布残差一起利用非线性最小二乘优化得到定位结果。与基线(NDT)方法相比,我们提出的方法在高速公路场景定位问题上的精度提高了34%。此外,我们的实验表明,收敛区域显著扩大,提高了自动驾驶汽车定位算法在高速公路场景下的可用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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