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

arXiv: https://arxiv.org/abs/1810.01619 · HTML: https://mlsanity.com/paper/1810.01619/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2018.

## Built on by (most cited first, 2 of 2)

- [The Reality Gap in Robotics: Challenges, Solutions, and Best Practices](https://mlsanity.com/paper/2510.20808/) (2025)
- [Deep Bayesian ICP Covariance Estimation](https://mlsanity.com/paper/2202.11607/) (2022)

## Abstract (arXiv)

In a context of 3D mapping, it is very important to get 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 generalises 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 go up to 20 cm for high incidence angles. We then used our modelisations to remove the bias from the measurements, leading to more accurate maps and a reduced localisation drift.