  The accelerating pace in the automation of agricultural tasks demands highly
accurate and robust localization systems for field robots. Simultaneous
Localization and Mapping (SLAM) methods inevitably accumulate drift on
exploratory trajectories and primarily rely on place revisiting and loop
closing to keep a bounded global localization error. Loop closure techniques
are significantly challenging in agricultural fields, as the local visual
appearance of different views is very similar and might change easily due to
weather effects. A suitable alternative in practice is to employ global sensor
positioning systems jointly with the rest of the robot sensors. In this paper
we propose and implement the fusion of global navigation satellite system
(GNSS), stereo views, and inertial measurements for localization purposes.
Specifically, we incorporate, in a tightly coupled manner, GNSS measurements
into the stereo-inertial ORB-SLAM3 pipeline. We thoroughly evaluate our
implementation in the sequences of the Rosario data set, recorded by an
autonomous robot in soybean fields, and our own in-house data. Our data
includes measurements from a conventional GNSS, rarely included in evaluations
of state-of-the-art approaches. We characterize the performance of
GNSS-stereo-inertial SLAM in this application case, reporting pose error
reductions between 10% and 30% compared to visual-inertial and loosely coupled
GNSS-stereo-inertial baselines. In addition to such analysis, we also release
the code of our implementation as open source.
