Evaluation Technique of 3D Point Clouds for Autonomous Vehicles Using the Convergence of Matching Between the Points
Evaluation Technique of 3D Point Clouds for Autonomous Vehicles Using the Convergence of Matching Between the Points
复制标题
利用点间匹配收敛性的自动驾驶车辆 3D 点云评估技术
DOI：
10.1109/sii46433.2020.9026196
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
J. Meguro
中科院分区：
文献类型：
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作者：
Takaya Murakami;Yuki Kitsukawa;E. Takeuchi;Y. Ninomiya;J. Meguro
在本文中，我们提出了一种新的地图评估技术，自动驾驶汽车使用的三维点云。自动驾驶中的定位是一项重要技术。注意力集中在精确的3D地图和点云数据上，因为需要这些地图数据来估计车辆位置。然而，所构造的3D点群可能由于测量而具有误差。在地形没有明显特征的地方，定位也会失败。我们的技术侧重于本地化过程来评估地图。我们建议的目标是计算本地化成功或失败的概率。该评估方法使用匹配收敛。评估测试表明，定位的地方是可能的，并在地图上的错误仍然可以清楚地分开的地方。在未来，输入的三维点云的范围内，适用于定位的范围，我们评估所提出的方法的有效性，通过增加集合。
In this paper, we propose a new map evaluation technique for autonomous vehicles using a 3D point cloud. Localization in autonomous driving is an important technology. Attention is focused on accurate 3D mapping and point cloud data, because this map data is needed to estimate vehicle position. However, the constructed 3D point group may have errors due to the measurement. Localization has also been known to fail in places where the terrain has few distinct features. Our technique focuses on localization process to evaluate the map. The goal of our proposal is to calculate the probability of success or failure of localization. This evaluation method uses convergence by matching. Evaluation tests showed that the places where the localization is possible, and the place where the error remains on the map can be clearly separated. In future, the range of the input 3D point cloud is made into the range applicable to Localization, and we evaluate the validity of the proposed method by increasing the set.