AM-Align is a globally optimal method for solving the accelerometer-magnetometer(AM) alignment problem. The package provides a complete set of algorithms, including intrinsic calibration, making it a convenient solution for improving the accuracy and robustness of inertial navigation systems.
Intrinsic calibration | AM-Align |
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- 2024/09/10: Accept to IEEE Transactions on Instrumentation and Measurement (T-IM)
- 2024/08/17: Minor revision.
- 2024/06/17: Major revision.
- 2024/03/26: Submitted as a Journal paper.
- 2024/03/20: Submitted to ICRA 2024 Workshop on Construction Robotics.
- Globally optimal solution for accelerometer-magnetometer alignment
- Robust to insufficient and outlier-corrupted data
- Requires only four pairs of measurements for complete calibration
- Efficient and accurate computation using the polynomial eigenvalue technique
- C++11 or newer
- CMake 3.10 or newer
- Eigen3
- Clone the repository:
git clone https://github.com/JokerJohn/AM_Align
- Build the project:
cd AM-Align
mkdir build && cd build
cmake ..
make
-
Prepare your accelerometer and magnetometer data in the required format.
-
Run the intrinsic calibration:
./am_align_calibration --acc_data /path/to/acc_data --mag_data /path/to/mag_data
- Run the AM-Align alignment estimation:
./am_align --acc_data /path/to/calibrated_acc_data --mag_data /path/to/calibrated_mag_data
- Integrate the estimated alignment into your LIO initialization process.
For more detailed usage instructions and examples, please refer to the documentation.
We welcome contributions to AM-Align! If you have any ideas, suggestions, or bug reports, please open an issue or submit a pull request. For major changes, please discuss them with the authors first.
Before contributing, please read our contributing guidelines and code of conduct.
AM-Align is released under the MIT License.
If you use AM-Align in your research, please cite our paper:
@article{xhu2024amalign,
title={AM-Align: Globally Optimal Accelerometer-Magnetometer Alignment},
author={Xiangcheng Hu*, Jin Wu*, Bohuan Xue, Yilong Zhu, Mingkai Jia, Yuhua Qi, Yi Jiang, Ping Tan and Wei Zhang},
journal={arxiv},
year={2024},
publisher={arxiv}
}
We would like to thank the following authors for their contributions and support.
For questions, comments, or suggestions, please contact [email protected].