Lidar measurement bias estimation via return waveform modelling in a context of 3D mapping

See also the post on Norlab!

Abstract

In a context of 3D mapping, it is very important to obtain accurate measurements from sensors. In particular, Light Detection And Ranging (LIDAR) measurements are typically treated as a zero-mean Gaussian distribution. We show that this assumption leads to predictable localisation drifts, especially when a bias related to measuring obstacles with high incidence angles is not taken into consideration. Moreover, we present a way to physically understand and model this bias, which generalizes to multiple sensors. Using an experimental setup, we measured the bias of the Sick LMS151, Velodyne HDL-32E, and Robosense RS-LiDAR-16 as a function of depth and incidence angle, and showed that the bias can reach 20 cm for high incidence angles. We then used our model to remove the bias from the measurements, leading to more accurate maps and a reduced localisation drift.

Source code and Datasets

The code source associated to this publication has been made open source and is publicly available here, for reproducibility and broad accessibility.

Citation

@inproceedings{Laconte2019bias,
	title 	= {Lidar measurement bias estimation via return waveform modelling in a context of 3D mapping},
	author 	= {Laconte, Johann and Desch{\^{e}}nes, Simon Pierre and Labussi{\`{e}}re, Mathieu and Pomerleau, Fran{\c{c}}ois},
	booktitle = {Proceedings - IEEE International Conference on Robotics and Automation},
	doi 	= {10.1109/ICRA.2019.8793671},
	isbn 	= {9781538660263},
	issn 	= {10504729},
	keywords = {3D Mapping,Bias Estimation,LIDAR,Sensor Error Modelling,Waveform Modelling},
	pages 	= {8100--8106},
	volume 	= {2019-May},
	year 	= {2019}
}