Registration of Inconsistent Point Cloud Maps with Large Scale Persistent Features

Simon Thompson, Masahi Yokozuka, Naohisa Hashimoto

2018

Abstract

Accurate point cloud registration techniques such as Iterative Closest Point matching have been developed to produce large scale 3D maps of the environment. Typically they iteratively register point clouds captured from adjacent sensor scans resulting in point clouds which are largely consistent. However, merging two seperate point cloud maps constructed at different times can lead to significant inconsistencies between the point clouds. Existing point based registration techniques can be sensitive to local minima caused by such inconsistencies. Feature based approaches can overcome local minimum but are typically less accurate, and can still suffer from correspondence errors. We introduce Large Scale Persistent Features (LSPFs), sub regions of point clouds that have orthogonal planar regions that are consistent and persist over a large spatial area. Each LSPF is used to calculate an individual transformation estimate using traditional registration techniques. Sampling Consensus is then used to select the best transform which is used for registration, avoiding local minima. LSPF registration is applied to simulated point cloud maps with known inconsistencies and shown to perform with more accuracy and lower computation time than other popular approaches. In addition, real world registration results are presented which demonstrate LSPF registration between MMS maps and low cost sensor maps captured 6 months apart.

Paper Citation

in Harvard Style

Thompson S. and Hashimoto N. (2018). Registration of Inconsistent Point Cloud Maps with Large Scale Persistent Features.In Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-989-758-321-6, pages 274-282. DOI: 10.5220/0006847602740282

in Bibtex Style

@conference{icinco18,
author={Simon Thompson and Naohisa Hashimoto},
title={Registration of Inconsistent Point Cloud Maps with Large Scale Persistent Features},
booktitle={Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2018},
pages={274-282},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006847602740282},
isbn={978-989-758-321-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - Registration of Inconsistent Point Cloud Maps with Large Scale Persistent Features
SN - 978-989-758-321-6
AU - Thompson S.
AU - Hashimoto N.
PY - 2018
SP - 274
EP - 282
DO - 10.5220/0006847602740282