FLIC: Fast Lidar Image Clustering

Frederik Hasecke, Frederik Hasecke, Lukas Hahn, Lukas Hahn, Anton Kummert

2021

Abstract

In this work, we propose an algorithmic approach for real-time instance segmentation of Lidar sensor data. We show how our method uses the underlying way of data acquisition to retain three-dimensional measurement information, while being narrowed down to a two-dimensional binary representation for fast computation. Doing so, we reframe the three-dimensional clustering problem to a two-dimensional connected-component labelling task. We further introduce what we call Map Connections, to make our approach robust against over- segmenting instances and improve assignment in cases of partial occlusions. Through detailed evaluation on public data and comparison with established methods, we show that these aspects improve the segmentation quality beyond the results offered by other three-dimensional cluster mechanisms. Our algorithm can run at up to 165 Hz on a 64 channel Velodyne Lidar dataset on a single CPU core.

Paper Citation

in Harvard Style

Hasecke F., Hahn L. and Kummert A. (2021). FLIC: Fast Lidar Image Clustering.In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-486-2, pages 25-35. DOI: 10.5220/0010193700250035

in Bibtex Style

@conference{icpram21,
author={Frederik Hasecke and Lukas Hahn and Anton Kummert},
title={FLIC: Fast Lidar Image Clustering},
booktitle={Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2021},
pages={25-35},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010193700250035},
isbn={978-989-758-486-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - FLIC: Fast Lidar Image Clustering
SN - 978-989-758-486-2
AU - Hasecke F.
AU - Hahn L.
AU - Kummert A.
PY - 2021
SP - 25
EP - 35
DO - 10.5220/0010193700250035