In autonomous vehicles, 3-D point cloud (PCD) maps are widely employed. By matching a point cloud acquired from a 3-D ranging sensor in real time with the PCD map, the ego vehicle can be localized with high accuracy. However, the PCD maps must be compressed and customized to the vehicles because they typically have low computing power, a small memory space, and low-resolution sensors. In this study, we propose an edge service of PCD map compression for connected intelligent vehicles. We overview a general path-aware map compression framework and propose a novel compression method to combine voxelization and a notion of localizability at every waypoint on target paths. Experimental results show that the proposed compression significantly reduces the computational cost at both the edge server and the vehicle while satisfying a required localization performance level.

中文翻译：

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适用于互联智能车辆的 3D 点云地图压缩

在自动驾驶汽车中，广泛采用 3D 点云 (PCD) 地图。通过将从 3D 测距传感器实时获取的点云与 PCD 地图进行匹配，可以高精度地定位自我车辆。然而，PCD 地图必须根据车辆进行压缩和定制，因为它们通常具有低计算能力、小存储空间和低分辨率传感器。在本研究中，我们提出了一种用于互联智能车辆的 PCD 地图压缩边缘服务。我们概述了通用的路径感知地图压缩框架，并提出了一种新颖的压缩方法，将体素化和目标路径上每个航路点的可定位性概念结合起来。实验结果表明，所提出的压缩显着降低了边缘服务器和车辆的计算成本，同时满足所需的定位性能水平。