Efficient Submap-based Autonomous MAV Exploration using VI-SLAM for LiDARs or Depth Cameras

Efficient Submap-based Autonomous MAV Exploration using Visual-Inertial SLAM Configurable for LiDARs or Depth Cameras
Sotiris Papatheodorou*, Simon Boche*, Sebastián Barbas Laina, Stefan Leutenegger
Autonomous exploration of unknown space is an
essential component for the deployment of mobile robots in the
real world. Safe navigation is crucial for all robotics applications
and requires accurate and consistent maps of the robot’s
surroundings. To achieve full autonomy and allow deployment
in a wide variety of environments, the robot must rely on on-
board state estimation which is prone to drift over time. We
propose a Micro Aerial Vehicle (MAV) exploration framework
based on local submaps to allow retaining global consistency by
applying loop-closure corrections to the relative submap poses.
To enable large-scale exploration we efficiently compute global,
environment-wide frontiers from the local submap frontiers and
use a sampling-based next-best-view exploration planner. Our
method seamlessly supports using either a LiDAR sensor or a
depth camera, making it suitable for different kinds of MAV
platforms. We perform comparative evaluations in simulation
against a state-of-the-art submap-based exploration framework
to showcase the efficiency and reconstruction quality of our
approach. Finally, we demonstrate the applicability of our
method to real-world MAVs, one equipped with a LiDAR and
the other with a depth camera.
*Equal contribution
Paper: arxiv.org/abs/...