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Code for "Event-Frame-Inertial Odometry Using Point and Line Features Based on Coarse-to-Fine Motion Compensation" in IEEE RA-L 2025

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Event-Frame-Inertial Odometry Using Point and Line Features Based on Coarse-to-Fine Motion Compensation (C2F-EFIO)

Authors : Byeongpil Choi, Hanyeol Lee, and Chan Gook Park

1. Overview

This repository contains the ROS package for Event-Frame-Inertial Odometry Using Point and Line Features Based on Coarse-to-Fine Motion Compensation (C2F-EFIO), written in C++. [Paper]

2. Build

  • This package was tested on Ubuntu 16.04 (ROS Kinetic) with C++11.
  • Dependencies:
    • Eigen 3.3.7
    • OpenCV 3.3.1
    • Boost 1.58.0.1
  • No additional dependencies are required, and we expect this package to build without issues in other environments.
  • The package uses the catkin build system:
cd catkin_ws
catkin_make
  • Expected directory structure:
catkin_ws
│   src
│     ├── config
│     ├── dvs_msgs
│     ├── mono_tracker
│     ├── msckf_estimator
│     └── rviz
│   build
│   devel
│   dataset
│     ├── ECDS
│     │   ├── boxes_6dof.bag
│     │   ├── ...
│     │   ├── shapes_translation.bag 
│     └── droneracing
│         ├── indoor_45_2_davis_with_gt.bag
│         ├── ...
│         ├── indoor_forward_10_davis_with_gt.bag 

3. Run

  • Prepare the visual-inertial calibration file in the config/ folder and set the appropriate ROS topics in msckf_estimator/launch/.
  • Launch files:

    • msckf_estimator/smsckf_nesl_ECDS.launch: Event-Camera Dataset
    • msckf_estimator/smsckf_nesl_droneracing_IF.launch: UZH-FPV Drone Racing Indoor Forward Dataset
    • msckf_estimator/smsckf_nesl_droneracing_I45.launch: UZH-FPV Drone Racing Indoor 45-Degree Dataset
    • msckf_estimator/smsckf_nesl_rviz.launch: RViz Launch

    Parameters setting in .launch files:

    max_cnt: The maximum number of point features to be tracked
    max_events: The number of events used to create the initial event frame (adaptively adjusted during runtime)
    life_pixel: The number of pixels a feature is allowed to move between consecutive event frames to calculate the lifetime of event point features
    eta: A ratio factor used to determine the optimal window size for extracting event lines
    pix_th: A threshold for selecting informative event points
    ransac_iter: The maximum number of iterations for RANSAC-based line fitting
    dist_thr: The distance threshold used for line outlier rejection tests
    theta_thr: The angle threshold used for line outlier rejection tests
    
  • Launch the launch files

    roslaunch smsckf_nesl smsckf_nesl_ECDS.launch --screen
    roslaunch smsckf_nesl smsckf_nesl_rviz.launch
    rosbag play /root/catkin_ws/dataset/ECDS/boxes_6dof.bag       # prepared dataset
    
  • Save the results

    rosbag record /smsckf_nesl/odom : img-synced output
    

4. Results

  • Line detection
  • Pose estimation

5. Citation

If you feel this work helpful to your academic research, we kindly ask you to cite our paper :

@article{C2F-EFIO_RAL,
  title={Event-Frame-Inertial Odometry Using Point and Line Features Based on Coarse-to-Fine Motion Compensation},
  author={Choi, Byeongpil and Lee, Hanyeol and Park, Chan Gook},
  journal={IEEE Robotics and Automation Letters},
  year={2025},
  publisher={IEEE}
}

6. Acknowledgements

This research was supported by the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT, the Republic of Korea. (NRF-2022R1A2C2012166)

7. License

Our source code is released under MIT license. If you encounter any issues or have questions, please contact the author at bpc1224@snu.ac.kr.

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