FLYBO: A Unified Benchmark Environment for Autonomous Flying Robots

Anthony Brunel, Amine Bourki, Olivier Strauss, Cedric Demonceaux

Abstract:

The use of Micro-Aerial Vehicles (MAVs) equipped with
odometry- and depth sensors has become predominant for a
wide variety of challenging industrial applications such as
the autonomous exploration (i.e., digital mapping), and inspection (i.e., online surface reconstruction) of unknown facilities. However, despite the ongoing attention these topics
receive, autonomous exploration systems still lack common
evaluation grounds to assess their relative performance in
terms of data and experimental tools. We address this
deficit by introducing FLYBO, the first unified benchmark
environment that focuses on the performance of such flying
robots in terms of autonomous exploration and online surface reconstruction. It includes (i) 11 challenging realistic
indoor- and outdoor datasets of increasing complexity and
size, with ground-truth, (ii) a comprehensive benchmark of
7 of the top-performing autonomous exploration algorithms
including methods without publicly available code. (iii) A
unified experimental system factorizes the routines shared
by autonomous planners in order to fairly and accurately
assess their exploration performance in a controlled environment.