This codebase implements my Interdisciplinary Project (IDP) Explanation of Uncertainty in Point Cloud Registration at Technical University of Munich. It uses kernel SHAP to find the associations between uncertainty sources and estimated uncertainty in Iterative Closest Point (ICP) algorithms.
After creating a Python virtual environment using conda
, pyenv virtualenv
, or venv
, do pip install -r requirements.txt
.
Then, clone the fork of libpointmatcher
by 3D ICP.
cd 3d-icp-cov
git clone https://github.com/CAOR-MINES-ParisTech/libpointmatcher.git
cd libpointmatcher
mkdir build && cd build
cmake ..
make
sudo make install
cd ../..
First, then download global_frame
and local_frame
datasets from the Challenging datasets at .
Then, create a directory data/<sequence>/
, and put the global_frame
and local_frame
datasets under data/<sequence>/global_frame/
and data/<sequence>/local_frame/0_0/
.
Last, change variables lpm_path
, dir_path
and results_base
of class Param
in utils.py
according to your paths.
After running ICP algorithms with different perturbations (see next section), the file structure would look like this:
├── data
│ ├── Apartment
│ │ ├── global_frame
│ │ │ ├── overlap_apartment.csv
│ │ │ ├── PointCloud<id>.csv
│ │ │ ├── pose_scanner_leica.csv
│ │ │ ├── xsens_Compass_mean.csv
│ │ │ ├── xsens_Gps_mean.csv
│ │ │ └── xsens_Gravity_mean.csv
│ │ ├── local_frame
│ │ │ ├── 0_0
│ │ │ │ ├── Hokuyo_<id>.csv
│ │ │ │ ├── <partial overlap>
│ │ │ │ │ ├── Hokuyo_<id>.csv
│ │ │ ├── <other sensor noise>
│ │ │ │ ├── <partial overlap>
│ │ │ │ │ ├── Hokuyo_<id>.csv
Both experiments in the paper can be run. Experiment 1 determines the relationship between perturbation levels of uncertainty sources and uncertainty estimate for point clouds scan_ref
and scan_ref+1
in sequence Apartment
. Experiment 2 determines the effects of uncertainty sources for contiguous point clouds in all sequences.
To run experiment 1, do:
python background.py <scan_ref> # run background dataset
python kernel_shap.py <scan_ref> # use kernel SHAP to generate plots
To run experiment 2, do:
python avg_parallel.py <seq> # the parallel version of average.py
python ks_avg_parallel.py <seq> # the parallel version of ks_avg.py
python plot_avg.py # visualize the inlier SHAP values
One could set logger
in /home/parallels/Desktop/idp/libpointmatcher/martin/config/base_config.yaml
to NullLogger
to avoid seeing output from ICP.