  Metric ground navigation addresses the problem of autonomously moving a robot
from one point to another in an obstacle-occupied planar environment in a
collision-free manner. It is one of the most fundamental capabilities of
intelligent mobile robots. This paper presents a standardized testbed with a
set of environments and metrics to benchmark difficulty of different scenarios
and performance of different systems of metric ground navigation. Current
benchmarks focus on individual components of mobile robot navigation, such as
perception and state estimation, but the navigation performance as a whole is
rarely measured in a systematic and standardized fashion. As a result,
navigation systems are usually tested and compared in an ad hoc manner, such as
in one or two manually chosen environments. The introduced benchmark provides a
general testbed for ground robot navigation in a metric world. The Benchmark
for Autonomous Robot Navigation (BARN) dataset includes 300 navigation
environments, which are ordered by a set of difficulty metrics. Navigation
performance can be tested and compared in those environments in a systematic
and objective fashion. This benchmark can be used to predict navigation
difficulty of a new environment, compare navigation systems, and potentially
serve as a cost function and a curriculum for planning-based and learning-based
navigation systems. We have published our dataset and the source code to
generate datasets for different robot footprints at
www.cs.utexas.edu/~xiao/BARN/BARN.html.
