  Exploration is a fundamental problem in robot autonomy. A major limitation,
however, is that during exploration robots oftentimes have to rely on on-board
systems alone for state estimation, accumulating significant drift over time in
large environments. Drift can be detrimental to robot safety and exploration
performance. In this work, a submap-based, multi-layer approach for both
mapping and planning is proposed to enable safe and efficient volumetric
exploration of large scale environments despite odometry drift. The central
idea of our approach combines local (temporally and spatially) and global
mapping to guarantee safety and efficiency. Similarly, our planning approach
leverages the presented map to compute global volumetric frontiers in a
changing global map and utilizes the nature of exploration dealing with partial
information for efficient local and global planning. The presented system is
thoroughly evaluated and shown to outperform state of the art methods even
under drift-free conditions. Our system, termed GLoca}, will be made available
open source.
