  In this paper we propose a planner for 3D exploration that is suitable for
applications using state-of-the-art 3D sensors such as lidars, which produce
large point clouds with each scan. The planner is based on the detection of a
frontier - a boundary between the explored and unknown part of the environment
- and consists of the algorithm for detecting frontier points, followed by
clustering of frontier points and selecting the best frontier point to be
explored. Compared to existing frontier-based approaches, the planner is more
scalable, i.e. it requires less time for the same data set size while ensuring
similar exploration time. Performance is achieved by not relying on data
obtained directly from the 3D sensor, but on data obtained by a mapping
algorithm. In order to cluster the frontier points, we use the properties of
the Octree environment representation, which allows easy analysis with
different resolutions. The planner is tested in the simulation environment and
in an outdoor test area with a UAV equipped with a lidar sensor. The results
show the advantages of the approach.
