lidar_simulator

Overview

This is a LiDAR simulator package for spinning LiDARs and solid-state LiDARs, described in paper: 3D LiDAR Intrinsic Calibration and Automatic System for LiDAR to Camera Calibration (PDF). More detail will be added soon.

Author: Bruce JK Huang Maintainer: Bruce JK Huang, bjhuang@umich.edu
Affiliation: The Biped Lab, the University of Michigan

This package has been tested under MATLAB2019a and Ubuntu 16.04.
[Issues] If you encounter any issues, I would be happy to help. If you cannot find a related one in the existing issues, please open a new one. I will try my best to help!

Abstract

Periodic intrinsic and extrinsic (re-)calibrations are essential for modern perception and navigation systems deployed on autonomous robots. To date, intrinsic calibration models for LiDARs have been based on hypothesized physical mechanisms for how a spinning LiDAR functions, resulting in anywhere from three to ten parameters to be estimated from data. Instead we propose to abstract away from the physics of a LiDAR type (spinning vs solid state, for example) and focus on the spatial geometry of the point cloud generated by the sensor. This leads to a unifying view of calibration. In experimental data, we show that it outperforms physics-based models for a spinning LiDAR. In simulation, we show how this perspective can be applied to a solid state LiDAR. We complete the paper by reporting on an open- source automatic system for target-based extrinsic calibration from a LiDAR to a camera.

Introduction Video

Please checkout the introduction video. It highlights some importants keypoints in the paper!

Results Quick View

Simulation of a spinning LiDAR (Velodyne LiDAR for example)

Simulation of a solid-state LiDAR

1. Noise-free:

2. Assume the plane of the wafer is slightly warped:

3. After calibration using the method described in paper: **3D LiDAR Intrinsic Calibration and Automatic System for LiDAR to Camera Calibration** ([PDF](https://github.com/UMich-BipedLab/lidar_simulator))

Calibration Results Quick View

Calibrate two types of LiDARs in the simulator using the method described in paper: 3D LiDAR Intrinsic Calibration and Automatic System for LiDAR to Camera Calibration (PDF)

Induce 3-parameter perturbation model (N1), 6-parameter perturbation model (N2), and Sim(3) noise model (N3) for a spinning LiDAR in the simulator.

Geometric uncertainty of a solid-state LiDAR by assuming the plane of the wafer is slightly warped.

Required packages

Please download matlab_utils from here.
Please download LiDAR_intrinsic_calibration from here.

Usage

To test the simulator for a spinning lidar, run main_spinning_lidar.m
To test the simulator for a solid state lidar, run main_solid_state_lidar.m

Citations

1. Jiunn-Kai Huang, Chenxi Feng, Madhav Achar, Maani Ghaffari, and Jessy W. Grizzle, "3D LiDAR Intrinsic Calibration and Automatic System for LiDAR to Camera Calibration" (PDF)(arXiv will appear soon))

2. Jiunn-Kai Huang and J. Grizzle, "Improvements to Target-Based 3D LiDAR to Camera Calibration" (PDF)(arXiv)

@article{huang2020improvements,
  author={J. {Huang} and J. W. {Grizzle}},
  journal={IEEE Access}, 
  title={Improvements to Target-Based 3D LiDAR to Camera Calibration}, 
  year={2020},
  volume={8},
  number={},
  pages={134101-134110},}