  We present an efficient, elastic 3D LiDAR reconstruction framework which can
reconstruct up to maximum LiDAR ranges (60 m) at multiple frames per second,
thus enabling robot exploration in large-scale environments. Our approach only
requires a CPU. We focus on three main challenges of large-scale
reconstruction: integration of long-range LiDAR scans at high frequency, the
capacity to deform the reconstruction after loop closures are detected, and
scalability for long-duration exploration. Our system extends upon a
state-of-the-art efficient RGB-D volumetric reconstruction technique, called
supereight, to support LiDAR scans and a newly developed submapping technique
to allow for dynamic correction of the 3D reconstruction. We then introduce a
novel pose graph clustering and submap fusion feature to make the proposed
system more scalable for large environments. We evaluate the performance using
two public datasets including outdoor exploration with a handheld device and a
drone, and with a mobile robot exploring an underground room network.
Experimental results demonstrate that our system can reconstruct at 3 Hz with
60 m sensor range and ~5 cm resolution, while state-of-the-art approaches can
only reconstruct to 25 cm resolution or 20 m range at the same frequency.
