# Physics-based Simulation of Continuous-Wave LIDAR for Localization, Calibration and Tracking

arXiv: https://arxiv.org/abs/1912.01652 · HTML: https://mlsanity.com/paper/1912.01652/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2019.

## Built on by (most cited first, 5 of 5)

- [Probabilistic Inference of Simulation Parameters via Parallel Differentiable Simulation](https://mlsanity.com/paper/2109.08815/) (2021)
- [Inferring Articulated Rigid Body Dynamics from RGBD Video](https://mlsanity.com/paper/2203.10488/) (2022)
- [DiSECt: A Differentiable Simulator for Parameter Inference and Control in Robotic Cutting](https://mlsanity.com/paper/2203.10263/) (2022)
- [Comprehensive Assessment of LiDAR Evaluation Metrics: A Comparative Study Using Simulated and Real Data](https://mlsanity.com/paper/2511.02994/) (2025)
- [End-to-End LiDAR optimization for 3D point cloud registration](https://mlsanity.com/paper/2602.10492/) (2026)

## Abstract (arXiv)

Light Detection and Ranging (LIDAR) sensors play an important role in the perception stack of autonomous robots, supplying mapping and localization pipelines with depth measurements of the environment. While their accuracy outperforms other types of depth sensors, such as stereo or time-of-flight cameras, the accurate modeling of LIDAR sensors requires laborious manual calibration that typically does not take into account the interaction of laser light with different surface types, incidence angles and other phenomena that significantly influence measurements. In this work, we introduce a physically plausible model of a 2D continuous-wave LIDAR that accounts for the surface-light interactions and simulates the measurement process in the Hokuyo URG-04LX LIDAR. Through automatic differentiation, we employ gradient-based optimization to estimate model parameters from real sensor measurements.