Smoothed Normal Distribution Transform for Efficient Point Cloud Registration During Space Rendezvous

Léo Renaut, Heike Frei, Andreas Nüchter

2023

Abstract

Next to the iterative closest point (ICP) algorithm, the normal distribution transform (NDT) algorithm is becoming a second standard for 3D point cloud registration in mobile robotics. Both methods are effective, however they require a sufficiently good initialization to successfully converge. In particular, the discontinuities in the NDT cost function can lead to difficulties when performing the optimization. In addition, when the size of the point clouds increases, performing the registration in real-time becomes challenging. This work introduces a Gaussian smoothing technique of the NDT map, which can be done prior to the registration process. A kd-tree adaptation of the typical octree representation of NDT maps is also proposed. The performance of the modified smoothed NDT (S-NDT) algorithm for pairwise scan registration is assessed on two large-scale outdoor datasets, and compared to the performance of a state-of-the-art ICP implementation. S-NDT is around four times faster and as robust as ICP while reaching similar precision. The algorithm is thereafter applied to the problem of LiDAR tracking of a spacecraft in close-range in the context of space rendezvous, demonstrating the performance and applicability to real-time applications.

Paper Citation

in Harvard Style

Renaut L., Frei H. and Nüchter A. (2023). Smoothed Normal Distribution Transform for Efficient Point Cloud Registration During Space Rendezvous. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP; ISBN 978-989-758-634-7, SciTePress, pages 919-930. DOI: 10.5220/0011616300003417

in Bibtex Style

@conference{visapp23,
author={Léo Renaut and Heike Frei and Andreas Nüchter},
title={Smoothed Normal Distribution Transform for Efficient Point Cloud Registration During Space Rendezvous},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP},
year={2023},
pages={919-930},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011616300003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP
TI - Smoothed Normal Distribution Transform for Efficient Point Cloud Registration During Space Rendezvous
SN - 978-989-758-634-7
AU - Renaut L.
AU - Frei H.
AU - Nüchter A.
PY - 2023
SP - 919
EP - 930
DO - 10.5220/0011616300003417
PB - SciTePress