Initial implementation of this one:
T. Cieslewski, E. Kaufmann and D. Scaramuzza, "Rapid exploration with multi-rotors:
A frontier selection method for high
speed flight," 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),
Vancouver, BC, 2017, pp.
2135-2142.
doi: 10.1109/IROS.2017.8206030
Abstract:
Exploring and mapping previously unknown environments while avoiding collisions with obstacles is
a fundamental task for autonomous robots. In scenarios where this needs to be done rapidly,
multi-rotors are a good choice for the task, as they can cover ground at potentially very
high velocities. Flying at high velocities, however, implies the ability to rapidly
plan trajectories and to react to new information quickly. In this paper, we propose
an extension to classical frontier based exploration that facilitates exploration at
high speeds. The extension consists of a reactive mode in which the
multi-rotor rapidly selects a goal frontier from its field of view.
The goal frontier is selected in a way that minimizes the change in
velocity necessary to reach it. While this approach can increase
the total path length, it significantly reduces the exploration time, since the multi-rotor
can fly at consistently higher speeds.
and this one:
A. Bircher, M. Kamel, K. Alexis, H. Oleynikova and R. Siegwart,
"Receding Horizon "Next-Best-View" Planner for 3D Exploration,"
2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, 2016, pp. 1462-1468.
doi: 10.1109/ICRA.2016.7487281
Abstract:
This paper presents a novel path planning algorithm for the autonomous exploration of
unknown space using aerial robotic platforms. The proposed planner employs a receding
horizon “next-best-view” scheme: In an online computed random tree it finds the best branch,
the quality of which is determined by the amount of unmapped space that can be explored.
Only the first edge of this branch is executed at every planning step,
while repetition of this procedure leads to complete exploration results.
The proposed planner is capable of running online, onboard a robot with limited resources.
Its high performance is evaluated in detailed simulation studies as well as in a challenging
real world experiment using a rotorcraft micro aerial vehicle. Analysis on the computational
complexity of the algorithm is provided and its good scaling properties enable the handling
of large scale and complex problem setups.
initially inspired from: https://github.com/demplo/robot_exploration