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Accurate Collaborative Globally-Referenced Digital Mapping with Standard GNSS.

Lakshay Narula¹, Michael J Wooten², Matthew J Murrian³

¹Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX 78712, USA. lakshay.narula@utexas.edu.

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|August 1, 2018

Summary

Achieving accurate digital maps for automated driving is crucial. This study presents GEOSLAM, a system using standard Global Navigation Satellite System (GNSS) receivers to reach sub-50-cm accuracy in horizontal mapping.

Keywords:

SLAM sensor fusion vehicle localization

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Area of Science:

Robotics
Geomatics Engineering
Computer Vision

Background:

Connected and automated vehicles require precise digital maps for navigation.
Global referencing of these maps is essential for accurate positioning.
Low-cost Global Navigation Satellite System (GNSS) receivers are commonly used for mapping.

Purpose of the Study:

To explore the accuracy limits of globally-referenced digital maps using standard GNSS.
To present GEOSLAM, a pipeline designed to achieve these accuracy limits.
To analyze GNSS error sources impacting positioning accuracy.

Main Methods:

Developed a globally-referenced electro-optical simultaneous localization and mapping (GEOSLAM) pipeline.
Incorporated standard GNSS position estimates into a visual SLAM framework.
Merged digital maps from multiple sessions and jointly optimized structure and motion with GNSS measurements.

Main Results:

Identified asymptotic error averages as the key accuracy-limiting factor in standard GNSS positioning.
Demonstrated sub-50-cm horizontal accuracy in digital mapping after multiple sessions using standard GNSS.
Observed persistent biases in the vertical direction for GNSS positioning.

Conclusions:

Sub-50-cm horizontal accuracy for digital mapping is feasible with standard GNSS and the GEOSLAM pipeline.
GEOSLAM effectively integrates GNSS data within a visual SLAM framework for improved accuracy.
Further research may be needed to address vertical accuracy limitations.