Forked from: rpng/MINS
An efficient, robust, and tightly-coupled Multisensor-aided Inertial Navigation System (MINS) which is capable of flexibly fusing all five sensing modalities (IMU, wheel encoders, camera, GNSS, and LiDAR) in a filtering fashion by overcoming the hurdles of computational complexity, sensor asynchronicity, and intra-sensor calibration.
Exemplary use case of MINS:
- VINS (mono, stereo, multi-cam)
- GPS-IMU (single, multiple)
- LiDAR-IMU (single, multiple)
- wheel-IMU
- Camera-GPS-LiDAR-wheel-IMU or more combinations.
This project was built on top of the following libraries which are in the thirdparty folder.
- OpenVINS: Open-source filter-based visual-inertial estimator.
- ikd-tree: Incremental k-d tree.
- libpointmatcher: Modular Iterative Closest Point library based on libnabo
This code was written by the Robot Perception and Navigation Group (RPNG) at the University of Delaware. If you have any issues with the code please open an issue on our GitHub page with relevant implementation details and references. For researchers that have leveraged or compared to this work, please cite the following:
The publication reference will be updated soon.
@article{Lee2023arxiv,
title = {MINS: Efficient and Robust Multisensor-aided Inertial Navigation System},
author = {Woosik Lee and Patrick Geneva and Chuchu Chen and Guoquan Huang},
year = 2023,
journal = {arXiv preprint arXiv:2309.15390},
url = {https://github.com/rpng/MINS},
}The codebase and documentation is licensed under the GNU General Public License v3 (GPL-3). You must preserve the copyright and license notices in your derivative work and make available the complete source code with modifications under the same license (see this; this is not legal advice).