  This article presents AutoRally, a 1$:$5 scale robotics testbed for
autonomous vehicle research. AutoRally is designed for robustness, ease of use,
and reproducibility, so that a team of two people with limited knowledge of
mechanical engineering, electrical engineering, and computer science can
construct and then operate the testbed to collect real world autonomous driving
data in whatever domain they wish to study. Complete documentation to construct
and operate the platform is available online along with tutorials, example
controllers, and a driving dataset collected at the Georgia Tech Autonomous
Racing Facility. Offline estimation algorithms are used to determine parameters
for physics-based dynamics models using an adaptive limited memory joint state
unscented Kalman filter. Online vehicle state estimation using a factor graph
optimization scheme and a convolutional neural network for semantic
segmentation of drivable surface are presented. All algorithms are tested with
real world data from the fleet of six AutoRally robots at the Georgia Tech
Autonomous Racing Facility tracks, and serve as a demonstration of the
robot$'$s capabilities.
