Explain ICP Uncertainty

This codebase uses kernel SHAP to find the associations between uncertainty sources and estimated uncertainty in Iterative Closest Point (ICP) algorithms.

The short paper and full report are in the folder paper.

Installation

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 ../..

Data Preparation

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

Run Pipeline

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

Note

One could set logger in libpointmatcher/martin/config/base_config.yaml to NullLogger to avoid seeing output from ICP.