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

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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.