Crowdsourcing RTK:a new GNSS positioning framework for building spatial high-resolution atmospheric maps based on massive vehicle GNSS data

High-quality spatial atmospheric delay correction information is essential for achieving fast integer ambiguity resolution(AR)in precise positioning.However,traditional real-time precise positioning frameworks(i.e.,NRTK and PPP-RTK)depend on spatial low-resolution atmospheric delay correction through the expensive and sparsely distributed CORS network.This results in limited public appeal.With the mass production of autonomous driving vehicles,more cost-effective and widespread data sources can be explored to create spatial high-resolution atmospheric maps.In this study,we propose a new GNSS positioning framework that relies on dual base stations,massive vehicle GNSS data,and crowdsourced atmospheric delay correction maps(CAM).The map is easily produced and updated by vehicles equipped with GNSS receivers in a crowd-sourced way.Specifically,the map consists of between-station single-differenced ionospheric and tropospheric delays.We introduce the whole framework of CAM initialization for individual vehicles,on-cloud CAM maintenance,and CAM-augmented user-end positioning.The map data are collected and preprocessed in vehicles.Then,the crowdsourced data are uploaded to a cloud server.The massive data from multiple vehicles are merged in the cloud to update the CAM in time.Finally,the CAM will augment the user positioning performance.This framework forms a beneficial cycle where the CAM’s spatial resolution and the user positioning performance mutually improve each other.We validate the performance of the proposed framework in real-world experiments and the applied potency at different spatial scales.We highlight that this framework is a reliable and practical positioning solution that meets the requirements of ubiquitous high-precision positioning.

ARSlice:Head-Mounted Display Augmented with Dynamic Tracking and Projection

Computed tomography(CT)generates cross-sectional images of the body.Visualizing CT images has been a challenging problem.The emergence of the augmented and virtual reality technology has provided promising solutions.However,existing solutions suffer from tethered display or wireless transmission latency.In this paper,we present ARSlice,a proof-of-concept prototype that can visualize CT images in an untethered manner without wireless transmission latency.Our ARSlice prototype consists of two parts,the user end and the projector end.By employing dynamic tracking and projection,the projector end can track the user-end equipment and project CT images onto it in real time.The user-end equipment is responsible for displaying these CT images into the 3D space.Its main feature is that the user-end equipment is a pure optical device with light weight,low cost,and no energy consumption.Our experiments demonstrate that our ARSlice prototype provides part of six degrees of freedom for the user,and a high frame rate.By interactively visualizing CT images into the 3D space,our ARSlice prototype can help untrained users better understand that CT images are slices of a body.