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Cloud Update of Tiled Evidential Occupancy Grid Maps for the Multi-Vehicle Mapping

机译：平铺的证据占用网格图的云更新以用于多车辆映射

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摘要

Nowadays, many intelligent vehicles are equipped with various sensors to recognize their surrounding environment and to measure the motion or position of the vehicle. In addition, the number of intelligent vehicles equipped with a mobile Internet modem is increasing. Based on the sensors and Internet connection, the intelligent vehicles are able to share the sensor information with other vehicles via a cloud service. The sensor information sharing via the cloud service promises to improve the safe and efficient operation of the multiple intelligent vehicles. This paper presents a cloud update framework of occupancy grid maps for multiple intelligent vehicles in a large-scale environment. An evidential theory is applied to create the occupancy grid maps to address sensor disturbance such as measurement noise, occlusion and dynamic objects. Multiple vehicles equipped with LiDARs, motion sensors, and a low-cost GPS receiver create the evidential occupancy grid map (EOGM) for their passing trajectory based on GraphSLAM. A geodetic quad-tree tile system is applied to manage the EOGM, which provides a common tiling format to cover the large-scale environment. The created EOGM tiles are uploaded to EOGM cloud and merged with old EOGM tiles in the cloud using Dempster combination of evidential theory. Experiments were performed to evaluate the multiple EOGM mapping and the cloud update framework for large-scale road environment.
机译：如今，许多智能车辆都配备了各种传感器，以识别其周围环境并测量车辆的运动或位置。另外，配备有移动互联网调制解调器的智能车辆的数量正在增加。基于传感器和Internet连接，智能车辆能够通过云服务与其他车辆共享传感器信息。通过云服务共享传感器信息有望改善多种智能车辆的安全和高效运行。本文提出了在大型环境中用于多个智能车辆的乘员网格图的云更新框架。应用证据理论来创建占用栅格图，以解决传感器干扰，例如测量噪声，遮挡和动态物体。配备了LiDAR，运动传感器和低成本GPS接收器的多辆汽车基于GraphSLAM为通过轨迹创建了证据占用栅格地图（EOGM）。大地测量四叉树图块系统用于管理EOGM，它提供了通用的图块格式来覆盖大型环境。使用证据理论的Dempster组合将创建的EOGM切片上传到EOGM云，并与云中的旧EOGM瓦片合并。进行了实验，以评估用于大型道路环境的多个EOGM映射和云更新框架。

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