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Sensors 2018, 18(8), 2452;

Accurate Collaborative Globally-Referenced Digital Mapping with Standard GNSS

Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX 78712, USA
Department of Aerospace Engineering and Engineering Mechanics, The University of Texas at Austin, Austin, TX 78712, USA
Author to whom correspondence should be addressed.
Received: 16 June 2018 / Revised: 25 July 2018 / Accepted: 26 July 2018 / Published: 28 July 2018
(This article belongs to the Special Issue GNSS and Fusion with Other Sensors)
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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. View Full-Text
Keywords: vehicle localization; SLAM; sensor fusion vehicle localization; SLAM; sensor fusion

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Narula, L.; Wooten, J.M.; Murrian, M.J.; LaChapelle, D.M.; Humphreys, T.E. Accurate Collaborative Globally-Referenced Digital Mapping with Standard GNSS. Sensors 2018, 18, 2452.

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