Elastic and efficient LiDAR reconstruction for large-scale exploration
tasks
tasks
File(s)2010.09232v1.pdf (4.48 MB)
Working paper
Author(s)
Type
Working Paper
Abstract
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 sparsification and submap fusion feature to make our system
more scalable for large environments. We evaluate the performance using a
published dataset captured by a handheld mapping device scanning a set of
buildings, 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.
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 sparsification and submap fusion feature to make our system
more scalable for large environments. We evaluate the performance using a
published dataset captured by a handheld mapping device scanning a set of
buildings, 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.
Date Issued
2020-10-19
Citation
2020
Publisher
arXiv
Copyright Statement
© 2020 The Author(s)
Sponsor
Engineering & Physical Science Research Council (EPSRC)
SLAMcore Ltd
Engineering & Physical Science Research Council (E
Identifier
http://arxiv.org/abs/2010.09232v1
Grant Number
EP/N018494/1
n/a
DFR05870 (EP/R026173/1)
Subjects
cs.RO
cs.RO
Notes
8 pages, 8 figures
Publication Status
Published