Abstract
In recent decades Global Positioning Systems (GPS) have become a ubiquitous tool to support navigation. Traditional GPS has an error in the order of 10–15 m, which is adequate for many applications (e.g., vehicle navigation) but for many robotics applications lacks required accuracy. In this paper we describe a technique, FAGPS (Fiducial Augmented Global Positioning System) to periodically use fiducial markers to lower the GPS drift, and hence for a small time-period have a more accurate GPS determination. We describe results from simulations and from field testing in open-sky environments where horizontal GPS accuracy was improved from a twice the distance root mean square (2DRMS) error of 5.5 m to 2.99 m for a period of up-to 30 min.
Highlights
Global Positioning Systems (GPS) (Global Positioning System) is a global absolute navigation system which uses one-way time of arrival (TOA) ranging from a constellation of satellites to compute a reasonably accurate three-dimensional position [1]
These results demonstrate the feasibility of the proposed FAGPS scheme and the performance improvement over traditional non-corrected GPS
This paper has proposed and demonstrated a GPS augmentation scheme, FAGPS, which uses geolocated fiducal images within an environment which can be observed and used to reduce GPS
Summary
GPS (Global Positioning System) is a global absolute navigation system which uses one-way time of arrival (TOA) ranging from a constellation of satellites to compute a reasonably accurate three-dimensional position [1]. GPS (originally known as NAVSTAR GPS) is the oldest Global Navigation Satellite System (GNSS). A number of alternate systems exist or are being developed including the Russian Global Navigation Satellite System (GLONASS), the European. The majority of low cost receivers currently only support traditional GPS, the proliferation of these additional constellations and the prospect of improved positional accuracy has lead to the development of GNSS receivers which can provide positional estimates by augmenting data across a range of different satellite constellations [3]
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