A Dense Reference Network for Mass-Market Centimeter-Accurate Positioning

The quality of atmospheric corrections provided by a dense reference network for centimeter-accurate carrierphase differential GNSS (CDGNSS) positioning is investigated. A dense reference network (less than 20 km inter-station distance) offers significant benefits for mass-market users, enabling lowcost (including single-frequency) CDGNSS positioning with rapid integer ambiguity resolution. Precise positioning on a massmarket platform would significantly influence the world economy, ushering in a host of consumer-focused applications such as globally-registered augmented and virtual reality and improved all-weather safety and efficiency for intelligent transportation systems, applications which have so far been hampered by the several-meter-level errors in standard GNSS positioning. This contribution examines CDGNSS integer ambiguity resolution performance in terms of network correction uncertainty, and network correction uncertainty, in turn, in terms of network density. It considers the total error in network corrections: a sum of ionospheric, tropospheric, and reference station multipath components. The paper’s primary goal is to identify the network density beyond which mass-market users would see no further significant improvement in ambiguity resolution performance. It finishes by describing development and deployment of a low-cost dense reference network in Austin, Texas.Aerospace Engineering and Engineering Mechanic

Accuracy Limits for Globally-Referenced Digital Mapping Using Standard GNSS

Exchange of location and sensor data among connected and automated vehicles will demand accurate global referencing of the digital maps currently being developed to aid positioning for automated driving. This paper explores the limit of such maps’ globally-referenced position accuracy when the mapping agents are equipped with low-cost Global Navigation Satellite System (GNSS) receivers performing standard codephase-based navigation. The key accuracy-limiting factor is shown to be the asymptotic average of the error sources that impair standard GNSS positioning. Asymptotic statistics of each GNSS error source are analyzed through both simulation and empirical data to show that sub-50-cm accurate digital mapping is feasible in moderately urban environments in the horizontal plane after multiple mapping sessions with standard GNSS, but larger biases persist in the vertical direction.Aerospace Engineering and Engineering Mechanic

Accurate Collaborative Globally-Referenced Digital Mapping with Standard GNSS

Exchange of location and sensor data among connected and automated vehicles will demand accurate global referencing of the digital maps currently being developed to aid positioning for automated driving. This paper explores the limit of such maps’ globally-referenced position accuracy when the mapping agents are equipped with low-cost Global Navigation Satellite System (GNSS) receivers performing standard code-phase-based navigation, and presents a globally-referenced electro-optical simultaneous localization and mapping pipeline, called GEOSLAM, designed to achieve this limit. The key accuracy-limiting factor is shown to be the asymptotic average of the error sources that impair standard GNSS positioning. Asymptotic statistics of each GNSS error source are analyzed through both simulation and empirical data to show that sub-50-cm accurate digital mapping is feasible in the horizontal plane after multiple mapping sessions with standard GNSS, but larger biases persist in the vertical direction. GEOSLAM achieves this accuracy by (i) incorporating standard GNSS position estimates in the visual SLAM framework, (ii) merging digital maps from multiple mapping sessions, and (iii) jointly optimizing structure and motion with respect to time-separated GNSS measurements

TEX-CUP: The University of Texas Challenge for Urban Positioning

A public benchmark dataset collected in the dense urban center of the city of Austin, TX is introduced for evaluation of multi-sensor GNSS-based urban positioning. Existing public datasets on localization and/or odometry evaluation are based on sensors such as lidar, cameras, and radar. The role of GNSS in these datasets is typically limited to the generation of a reference trajectory in conjunction with a high-end inertial navigation system (INS). In contrast, the dataset introduced in this paper provides raw ADC output of wideband intermediate frequency (IF) GNSS data along with tightly synchronized raw measurements from inertial measurement units (IMUs) and a stereoscopic camera unit. This dataset will enable optimization of the full GNSS stack from signal tracking to state estimation, as well as sensor fusion with other automotive sensors. The dataset is available under Public Datasets. Efforts to collect and share similar datasets from a number of dense urban centers around the world are under way.Aerospace Engineering and Engineering Mechanic

ADAS Enhanced by 5G Connectivity [Project Title from Cover]

DTRT13-G-UTC58Volume 1: This paper explores the limit of digital maps\u2019 globally-referenced position accuracy when the mapping agents are equipped with low-cost Global Navigation Satellite System (GNSS) receivers performing standard codephase- based navigation, and presents a globally-referenced electro-optical simultaneous localization and mapping pipeline, called GEOSLAM, designed to achieve this limit. The key accuracy-limiting factor is shown to be the asymptotic average of the error sources that impair standard GNSS positioning. Asymptotic statistics of each GNSS error source are analyzed through both simulation and empirical data to show that sub-50-cm accurate digital mapping is feasible in the horizontal plane after multiple mapping sessions with standard GNSS, but larger biases persist in the vertical direction. GEOSLAM achieves this accuracy by (i) incorporating standard GNSS position estimates in the visual SLAM framework, (ii) merging digital maps from multiple mapping sessions, and (iii) jointly optimizing structure and motion with respect to time-separated GNSS measurements. Volume 2: A system developed for low-cost precise urban vehicular positioning is demonstrated to achieve a probability of correct integer fixing greater than 96.5% for a probability of incorrect integer fixing surely less than 2.3% and likely less than 1%. The results are achieved without any aiding by inertial or electro-optical sensors. Development and evaluation of the unaided GNSS-based precise positioning system is a key milestone toward the overall goal of combining precise GNSS, vision, radar, and inertial sensing for all-weather high-integrity precise positioning for automated and connected vehicles. All components have been tailored in their design to yield competent sub-decimeter positioning in the mobile urban environment. A performance sensitivity analysis reveals that navigation data bit prediction on the GPS L1 C/A signals is key to high-performance urban real-time kinematic (RTK) positioning