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.

• Publication reference - https://arxiv.org/pdf/2309.15390.pdf

Key Features

• Inertial(IMU)-based multi-sensor fusion including wheel odometry and arbitrary numbers of cameras, LiDARs, and GNSSs (+ VICON or loop-closure) for localization.
• Online calibration of all onboard sensors (check exemplary results).
• Consistent high-order state on manifold interpolation improved from our prior work (MIMC-VINS) and dynamic cloning strategy for light-weight estimation performance.
• Multi-sensor simulation toolbox for IMU, camera, LiDAR, GNSS, and wheel enhanced from our prior work (OpenVINS)
• Evaluation toolbox for consistency, accuracy, and timing analysis.
• Very detailed options for each sensor enabling general multi-sensor application.

Dependency

MINS is tested on Ubuntu 18 and 20 and only requires corresponding ROS (Melodic and Noetic).

• Default Eigen version will be 3.3.7 (Noetic) or lower, but if one has a higher version the compilation can be failed due to thirdparty library (libpointmatcher) for LiDAR.

Build and Source

mkdir -p $MINS_WORKSPACE/catkin_ws/src/ && cd $MINS_WORKSPACE/catkin_ws/src/
git clone https://github.com/rpng/MINS
cd .. && catkin build
source devel/setup.bash

Run Examples

Simulation

roslaunch mins simulation.launch cam_enabled:=true lidar_enabled:=true

Real-World Dataset

Directly reading the ros bag file

roslaunch mins rosbag.launch config:=kaist/kaist_LC path_gt:=urban30.txt path_bag:=urban30.bag

Here are the rosbag files and ground truths we used in the evaluation. To be specific, we used kaist2bag to convert all sensor readings to rosbag files. All rights reserved to KAIST urban dataset.

Rosbag	GT (csv)	GT (txt)	Rosbag	GT (csv)	GT (txt)
urban18.bag	urban18.csv	urban18.txt	urban19.bag	urban19.csv	urban19.txt
urban20.bag	urban20.csv	urban20.txt	urban21.bag	urban21.csv	urban21.txt
urban22.bag	urban22.csv	urban22.txt	urban23.bag	urban23.csv	urban23.txt
urban24.bag	urban24.csv	urban24.txt	urban25.bag	urban25.csv	urban25.txt
urban26.bag	urban26.csv	urban26.txt	urban27.bag	urban27.csv	urban27.txt
urban28.bag	urban28.csv	urban28.txt	urban29.bag	urban29.csv	urban29.txt
urban30.bag	urban30.csv	urban30.txt	urban31.bag	urban31.csv	urban31.txt
urban32.bag	urban32.csv	urban32.txt	urban33.bag	urban33.csv	urban33.txt
urban34.bag	urban34.csv	urban34.txt	urban35.bag	urban35.csv	urban35.txt
urban36.bag	urban36.csv	urban36.txt	urban37.bag	urban37.csv	urban37.txt
urban38.bag	urban38.csv	urban38.txt	urban39.bag	urban39.csv	urban39.txt

Subscribing to the ros messages

roslaunch mins subscribe.launch config:=euroc_mav rosbag:=V1_03_difficult.bag bag_start_time:=0

RViz

rviz -d mins/launch/display.rviz

Acknowledgements

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

Credit / Licensing

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).