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Conference Papers Year : 2019

Lidar Measurement Bias Estimation via Return Waveform Modelling in a Context of 3D Mapping

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.
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Dates and versions

hal-04589417 , version 1 (27-05-2024)

Identifiers

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Johann Laconte, Simon-Pierre Deschenes, Mathieu Labussière, François Pomerleau. Lidar Measurement Bias Estimation via Return Waveform Modelling in a Context of 3D Mapping. 2019 International Conference on Robotics and Automation (ICRA), May 2019, Montreal, Canada. pp.8100-8106, ⟨10.1109/ICRA.2019.8793671⟩. ⟨hal-04589417⟩
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