Implementation of ICCV 2017: Colored Point Cloud Registration Revisited
This repository serves as a learning material for ICP and Colored ICP algorithms. The code is well organized and clean. We aim to focus on only the main pipeline of the algorithm, and avoid complicated interfaces and nested templates as in large libraries such as PCL and Open3D.
config
folder
params.yaml
The YAML file to control the running flow of the point cloud registration process. We adopt a header-only library mini-yaml
in this project. It is convenient for tuning parameters without the need of re-compilation of the C++ program.data
folder
include
folder
color_icp/helper.h
Provide some helper functions that were developed in some other projects of mine. Only the loadPointCloud
function is used in this project. Feel free to make use of the rest of helper functions as you see fit.color_icp/remove_nan.h
Include some customized functions to remove NaN points in the point cloud; they are modified from PCL.color_icp/yaml.h
The header file adopted from the mini-yaml library.scripts
folder
colored_icp.py
A python script that runs ICP and Colored ICP algorithms using the API provided by Open3D. It can be used to compare the performance of our code with that of Open3D.src
folder
color_icp.cpp
The core implementation of the registration pipeline. It takes in the params.yaml
file and runs modular-designed functions accordingly.optimization.cpp
A simple practice code to solve a curve fitting problem using Gauss-Newton method.yaml.cpp
The cpp file adopted from the mini-yaml library.Notes_on_Colored_Point_Cloud_Registration.pdf
Some math notes about residuals and Jacobian matrices used in the Colored ICP algorithm.The code was developed under Ubuntu 18. When needed, PCL 1.8 is used (the default version under Ubuntu 18). We follow a typical compilation procedure using CMake.
mkdir build
cd build
cmake ..
make
The running flow is controlled via the params.yaml
file under the config
folder. Modify the YAML file as you like, and run
./color_icp
float
precision was not good enough and can cause numerical instability at convergence. This can be observed in the JTJ and JTr matrices. Switching to double
precision solved this issue.