  In this article, we propose a novel navigation framework that leverages a two
layered graph representation of the environment for efficient large-scale
exploration, while it integrates a novel uncertainty awareness scheme to handle
dynamic scene changes in previously explored areas. The framework is structured
around a novel goal oriented graph representation, that consists of, i) the
local sub-graph and ii) the global graph layer respectively. The local
sub-graphs encode local volumetric gain locations as frontiers, based on the
direct pointcloud visibility, allowing fast graph building and path planning.
Additionally, the global graph is build in an efficient way, using node-edge
information exchange only on overlapping regions of sequential sub-graphs.
Different from the state-of-the-art graph based exploration methods, the
proposed approach efficiently re-uses sub-graphs built in previous iterations
to construct the global navigation layer. Another merit of the proposed scheme
is the ability to handle scene changes (e.g. blocked pathways), adaptively
updating the obstructed part of the global graph from traversable to
not-traversable. This operation involved oriented sample space of a path
segment in the global graph layer, while removing the respective edges from
connected nodes of the global graph in cases of obstructions. As such, the
exploration behavior is directing the robot to follow another route in the
global re-positioning phase through path-way updates in the global graph.
Finally, we showcase the performance of the method both in simulation runs as
well as deployed in real-world scene involving a legged robot carrying camera
and lidar sensor.
