ROB101-Optimization Problem

Overview

This package demonstrates how to use the naive gradient decent algorithm and the Hessian method to calibrate a LiDAR and a camera. For more details about this package, please read the textbook at here.

• Authors: Bruce JK Huang and Jessy W. Grizzle
• Maintainer: Bruce JK Huang, brucejkh[at]gmail.com
• Affiliation: The Biped Lab, the University of Michigan

This package has been tested under MATLAB 2020a on Ubuntu 18.04, MacOs Catalina (10.15.7), and MacOs Big Sur (11.6).

How To Start

[Required Packages] Please install the following packages in your MATLAB:

• Image Processing Toolbox
• Phased Array System Toolbox
• ROS Toolbox

[Data Preparation] Please download the data from the Google Drive, and put them under the ROB101_data folder.

[Running] Directly run rob101_optimization.m. All the eight figures should pop up, and the results will be shown in the command window.
Here are the results shown in my command window with opt.Hessian = 1:

Loading data...
======================================
Optimizing using the Hessian method...
======================================
The initial cost is: 49750.06
After optimizing over 14 iterations, the final cost is: 12.11
Elapsed time is 0.092645 seconds.
Plotting restuls...
All Done!!

Note on parameters

opt.Hessian (0/1): <default: 1>
0: use the gradient descent method
1: use the hessian method

[Issues] If you encounter issues when running the code, please take a look at the issues opened. If there is related issue, please open a new one, I will try my best to help.

Citations

The detail is described in: 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},}