hoangvietdo / drone_vins_sim

Simulation for the paper "An Adaptive Approach based on Multi-State Constraint Kalman Filter for UAVs" published in ICCAS 2021

Drone Visual-Inertial Navigation System (VINS) Simulation

1. Overview

This is a drone simulation written in Matlab. The simulation employs a PID controller to guide a quadcopter along a given smooth trajectory and generate ground truth, IMU data, and monocular camera images using a pinhole model. For instance, we simulate the drone flying in a circular pattern assuming that the camera is oriented downward, capturing randomly generated ground features. The resulting dataset can be utilized to evaluate the performance of a visual-inertial navigation system (VINS).

2. Prerequisites

• Matlab R2022b.

3. Usage

• The /IMU/drone.m script generates the /Datasets/drone_IMU.mat dataset includes IMU measurements (Forward-Right-Down), IMU specifications, and ground truth data (Forward-Left-Up).
• The /Camera/makeCamera.m script generates ground features based on the data from /Datasets/drone_IMU.mat, records monocular camera data, and then combines all the information into the final dataset named /Datasets/drone_IMU_camera.mat.
• Additionally, we provide a simplified version of an Extended Kalman Filter for Visual-Inertial Navigation System (EKF-VINS) algorithm to validate the dataset.

4. Credit / Acknowledgements

• This code was written at the Intelligent Navigation and Control Systems Laboratory, Sejong University, Seoul, Republic of Korea.
• This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP2021-2018-0-01423) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation), and also be supported by the Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea Government (MOTIE) (N0002431, The Competency Development Program for Industry Specialist).

5. Citation

If you find this work beneficial to your academic research, we would greatly appreciate it if you could reference our paper in your citations.

@INPROCEEDINGS{do2021amsckf,
  author={Do, Hoang Viet and Kim, Yong Hun and Kwon, Yeong Seo and Kang, San Hee and Kim, Hak Ju and Song, Jin Woo},
  booktitle={2021 21st International Conference on Control, Automation and Systems (ICCAS)}, 
  title={An Adaptive Approach based on Multi-State Constraint Kalman Filter for UAVs}, 
  year={2021},
  volume={},
  number={},
  pages={481-485},
  doi={10.23919/ICCAS52745.2021.9649897}
}

6. License

Our source code is released under the MIT license. If there are any issues in our source code please contact the author [email protected].