A cost-effective mapping platform for digitizing the world: from autonomous vehicles to digital twins and the metaverse

术语数字孪生兄弟是指创建物理对象的虚拟副本，并在它们之间建立联系。这一数字孪生兄弟的概念已经嵌入到自动驾驶汽车、地理信息系统、协作虚拟空间等现代创新的结构中。这些技术产生了巨大的经济影响，预计到2026年，自动驾驶产业的价值将达到10亿美元。最近，Meta承诺在未来几年投资100亿美元创建Metverse，预计这将开启一个价值数千亿美元的创意经济。所有这些应用所需的骨干技术基础设施是真实世界/数字孪生兄弟的精确3D表示。使用地面激光雷达扫描仪生成测量级地图的大多数地图技术都极其昂贵和耗时。相反，移动地图平台除了价格昂贵外，还无法生成测量级地图。为了创建规模的数字孪生兄弟，这对自动驾驶和组合工作空间至关重要，地图平台需要低成本，同时还需要提供所需的精度。我们提出了一种低成本的测绘平台，由相机、激光雷达、GNSS接收器、惯性传感器组成，可以安装在任何平台(汽车、背包、无人机)上进行环境测绘。在可能的供资期间将开展的活动有：--系统校准：对所有传感器进行实验室和在线校准--定位：在全球导航卫星系统和全球导航卫星系统被拒绝的环境中进行地理登记的定位--物体探测和下游应用的原始数据分割--地图生成和三维重建：根据登记的数据创建三维地图。该平台将能够在全球导航卫星系统和全球导航卫星系统被拒绝的环境中，制作与自动驾驶车辆所需的高清地图兼容的地理信息登记的勘测级地图。

The term digital twin is to create a virtual copy of the physical object, with a connection between them. This concept of the digital twin has been embedded into the fabric of modern innovations such as autonomous vehicles, geographical information systems (GIS), collaborative virtual spaces, etc. These technologies have an enormous economic impact with the autonomous industry expected to be worth $64 billion by 2026. Recently Meta has promised to invest $10 billion into the creation of metaverse over the next few years which is expected to start a creative economy worth hundreds of billions of dollars. The backbone technological infrastructure needed for all these applications is a precise 3D representation of the real-world/digital twin.Most mapping technologies by using terrestrial LiDAR scanners for survey-grade map generation are extremely expensive and time-consuming. On the contrary, mobile mapping platforms cannot generate survey-grade maps in addition to being expensive. To create a digital twin at scale, which is essential for autonomous driving and combined workspaces, the mapping platform needs low cost while also providing needed accuracy. We propose a low-cost mapping platform consisting of cameras, LiDAR, GNSS receiver, an inertial sensor that can be mounted on any platform (automobiles, backpacks, UAVs) to map the environment. The activities that will be performed during the possible funding duration are, - System calibration: Laboratory and online calibration for all the sensors - Localization: Geo-registered localization in both GNSS and GNSS denied environments- Object detection and segmentation from the raw data for downstream applications - Map generation and 3D reconstruction: Creating a 3D map from the registered data. The platform will be able to produce geo registered, survey-grade maps that are also compatible with the HD Maps required by the autonomous vehicles, in both GNSS and GNSS denied environments.