BILTS Measure: Experimental Validation and Comparative Evaluation

This repository contains the MATLAB code for the experimental validation and comparative evaluation of the BILTS similarity measure against other approaches: DHB, eFS, ISA, ISA-ocp, RRV, and DSRF. The results and methodology are described in the accompanying paper (TO DO: add citation).

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Repository Structure

• src/: Contains the main script for running the trajectory recognition experiments: main_trajectory_recognition.m.
• Libraries/: Includes the required external libraries for the experiments:
  • casadi_library/: Users must download and place the Casadi library here (see instructions below).
• Data/Datasets/: Directory for datasets (folders are to be made and the datasets are to be downloaded separately, see instructions below).
• Generated Results: We also included an Excel file summarizing the results as reported in the paper.

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Prerequisites

1. MATLAB

Ensure that MATLAB is installed on your system. The code is tested with MATLAB R2024b.

2. Casadi Library

Download the Casadi library version casadi-windows-matlabR2016a-v3.5.5 from the Casadi website.
Place the downloaded library in the following directory:

Libraries/casadi_library/

3. Datasets

The code requires two datasets: DLA and SYN. Download these datasets from Zenodo. After downloading, create the directory Data/Datasets/, and place the datasets in the this directory:

Data/Datasets/DLA/
Data/Datasets/SYN/

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How to Run the Code

1. Download and set up the repository:

   git clone <repository-url>
   cd <repository-folder>

2. Download and place the Casadi library in Libraries/casadi_library/.
3. Download and organize the datasets in Data/Datasets/ as described above.
4. Open MATLAB and navigate to the src/ folder.
5. Run the main script:

   main_trajectory_recognition

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citing

If you use this package in your research, please cite the following paper preprint:

@misc{...,
      title={BILTS: A Bi-Invariant Similarity Measure for Robust Object Trajectory Recognition under Reference Frame Variations}, 
      author={Arno Verduyn and Erwin Aertbeliën and Glenn Maes and Joris De Schutter and Maxim Vochten},
      year={2025},
      eprint={2405.04392},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2405.04392}, 
}

Acknowledgments

This work uses the Casadi library and the datasets available on Zenodo. We acknowledge the creators of these resources for their contributions to the research community.