This repository implements a lightweight Python wrapper around two registration algorithms from the Point Cloud Library with minimal dependencies due to reliance on the Python standard library and the ubiquitous Numpy.
The approach is reasonable due to the need to embed fast and reliable registration capabilities in existing Python projects which is not given at the moment, as existing pure Python implementations are both too slow and poorly maintained.
The following registration algorithms are supported so far:
- Iterative Closest Point (ICP)
- Normal Distribution Transform (NDT)
In order to use the module with the pre-compiled library, you need to have Python 3 and Numpy installed.
As this code is only a thin wrapper around some of PCLs functionality, you will also have to install PCL itself. On Ubuntu, use
sudo apt install libpcl-dev
Please check out the official PCL website linked above for installation instructions on other architectures.
If you would like to make changes to the C++ code and compile it yourself you will additionally need to install the g++/c++ compiler and CMake.
The most straight forward way to use this module is to rely on the pre-compiled
library. Simply import the reglib module, load your data and run the registration
algorithm. To get started have a look at test.py.
So far, the module supports:
- Loading of CSV, PLY and PCD 3D point data.
- Uniform subsampling of the input data.
- Registration using either the ICP or NDT algorithm. Both return the 4x4 transformation matrix between the source and target point clouds in homogeneous coordinates.
- Visualization and interactive execution.