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首页> 外文会议>2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems >LIDAR-data accumulation strategy to generate high definition maps for autonomous vehicles
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LIDAR-data accumulation strategy to generate high definition maps for autonomous vehicles

机译：LIDAR数据累积策略可为自动驾驶汽车生成高清地图

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摘要

Mapping is a very critical issue for enabling autonomous driving. This paper proposes a robust approach to generate high definition maps based on LIDAR point clouds and post-processed localization measurements. Many problems are addressed including quality, saving size, global labeling and processing time. High quality is guaranteed by accumulating and killing the sparsity of the point clouds in a very efficient way. The storing size is decreased using sub-image sampling of the entire map. The global labeling is achieved by continuously considering the top-left corner of the map images as a reference regardless to the driven distance and the vehicle orientation. The processing time is discussed in terms of using the generated maps in autonomous driving. Moreover, the paper highlights a method to increase the density of online LIDAR frames to be compatible with the intensity level of the generated maps. The proposed method was used since 2015 to generate maps of different areas and courses in Japan and USA with very high stability and accuracy.
机译：映射是实现自动驾驶的一个非常关键的问题。本文提出了一种基于LIDAR点云和后处理定位测量结果生成高清地图的可靠方法。解决了许多问题，包括质量，节省尺寸，全局标记和处理时间。通过以非常有效的方式累积和消除点云的稀疏性来保证高质量。使用整个地图的子图像采样可以减小存储大小。通过连续考虑地图图像的左上角作为参考来实现全局标记，而与行驶距离和车辆方向无关。根据在自动驾驶中使用生成的地图来讨论处理时间。此外，本文重点介绍了一种增加在线LIDAR帧密度以与生成的图的强度级别兼容的方法。自2015年以来，该方法已用于生成日本和美国不同地区和路线的地图，具有很高的稳定性和准确性。

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