SplatPlanner: Efficient Autonomous Exploration via Permutohedral Frontier Filtering

Anthony Brunel, Amine Bourki, Cedric Demonceaux, Olivier Strauss

Abstract:

We address the problem of autonomous
exploration of unknown environments using a Micro Aerial
Vehicle (MAV) equipped with an active depth sensor. As
such, the task consists in mapping the gradually discovered
environment while planning the envisioned trajectories in
real-time, using on-board computation only. To do so, we
present SplatPlanner, an end-to-end autonomous planner that
is based on a novel Permutohedral Frontier Filtering (PFF)
which relies on a combination of highly efficient operations
stemming from bilateral filtering using permutohedral lattices
to guide the entire exploration. In particular, our PFF is
computationally linear in input size, nearly parameter-free, and
aggregates spatial information about frontier-neighborhoods
into density scores in one single step. Comparative experiments
made on simulated environments of increasing complexity
show our method consistently outperforms recent state-of-theart methods in terms of computational efficiency, exploration
speed and qualitative coverage of scenes. Finally, we also
display the practical capabilities of our end-to-end system in
a challenging real-flight scenario.