Efficient Surfel-Based SLAM using 3D Laser Range Data in Urban Environments
Efficient Surfel-Based SLAM using 3D Laser Range Data in Urban Environments
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DOI：
10.15607/rss.2018.xiv.016
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发表时间：
2018-06
期刊：
影响因子：
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通讯作者：
J. Behley;C. Stachniss
中科院分区：
文献类型：
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作者：
J. Behley;C. Stachniss
准确可靠的定位和映射是大多数自主机器人的基本构建块。为此，我们提出了一种新的，密集的方法，以激光为基础的映射，从旋转激光传感器获得的三维点云上操作。我们构建了一个基于冲浪的地图，并估计机器人的姿态的变化，通过利用当前扫描和渲染模型视图之间的投影数据关联，从冲浪地图。对于环路闭合的检测和验证，我们利用地图表示在潜在环路闭合之前组成地图的虚拟视图，即使扫描和已映射区域之间的重叠较低，也可以进行更稳健的检测。我们的方法是有效的，并实现实时配准。同时，它能够检测环路闭合并以在线方式执行地图更新。我们的实验表明，我们能够估计全球一致的地图，在大规模的环境中，仅基于点云数据。
—Accurate and reliable localization and mapping is a fundamental building block for most autonomous robots. For this purpose, we propose a novel, dense approach to laser- based mapping that operates on three-dimensional point clouds obtained from rotating laser sensors. We construct a surfel-based map and estimate the changes in the robot’s pose by exploiting the projective data association between the current scan and a rendered model view from that surfel map. For detection and verification of a loop closure, we leverage the map representation to compose a virtual view of the map before a potential loop closure, which enables a more robust detection even with low overlap between the scan and the already mapped areas. Our approach is efficient and enables real-time capable registration. At the same time, it is able to detect loop closures and to perform map updates in an online fashion. Our experiments show that we are able to estimate globally consistent maps in large scale environments solely based on point cloud data.