LIO_SAM_6AXIS is an open-source SLAM project based on the project LIO_SAM that has been modified to support a wider range of sensors. It includes support for a 6-axis IMU and low-cost GNSS, making it easier to adapt for your own sensor setup.

LIO_SAM_6AXIS includes the following features:

• Support for a 6-axis IMU: This allows you to use orientation information in state estimation, improving the accuracy of your results.
• Support for low-cost GNSS: By eliminating the need to adapt for the robot_localization node, this feature makes it easier to integrate GNSS into your SLAM system.
• GPS constraint visualization: This feature helps with debugging by allowing you to visualize the GPS constraints that are being used in the optimization.
• Compatible with a range of lidars: LIO_SAM_6AXIS can be adapted to work with a range of lidars, including popular models like the VLP-16 ，Pandar32 and Ouster OS-1.
• Easy to adapt: With minor changes to the original code, LIO_SAM_6AXIS can be adapted to work with your own sensors and lidars.

To get started with LIO_SAM_6AXIS, follow these steps:

1. Clone the repository:

git clone https://github.com/JokerJohn/LIO_SAM_6AXIS.git

2. Install the dependencies:

cd LIO_SAM_6AXIS
catkin build

3. Launch the roslaunch file for your sensor setup:

roslaunch lio_sam_6axis run.launch

For more information on how to use LIO_SAM_6AXIS, see the video tutorial and documentation.

4. finally, save your point cloud map.

rosservice call /lio_sam_6axis/save_map

5. for docker support.

Dockerfile is for people who don't want to break their own environment.

# please cd the folder which have Dockerfile first, approximately 10mins based on your internet and CPU
docker build -t zhangkin/lio_sam_6axis .

docker run -it --net=host --gpus all --name lio_sam_6axis zhangkin/lio_sam_6axis /bin/zsh

# OR -v to link the folder from your computer into container (your_computer_loc:container_loc)
docker run -it --net=host --gpus all --name lio_sam_6axis -v /home/kin/bag_data:/home/xchu/data/ramlab_dataset zhangkin/lio_sam_6axis /bin/zsh

# in the container
catkin build
source devel/setup.zsh

# with dataset download and linked ==> please see more usage in previous section
roslaunch lio_sam_6axis ouster128_indoors.launch

# 对于在内地的同学，可以换源`dockerhub`后，直接拉取：
docker pull zhangkin/lio_sam_6axis

The documentation for LIO_SAM_6AXIS can be found in the doc directory of the repository. It includes instructions on how to adapt the code for your own sensors and lidars.

• Bilibili
• Youtube

Here are the latest updates to LIO_SAM_6AXIS:

• Fix some bugs in GNSS odometry: If the GNSS has enough translation (larger than 0.1m) in a short time, we publish an absolute yaw angle as a reference.
• Improve LIO-GPS initialization: If the GNSS trajectory has been aligned well with the LIO trajectory, we refine the LLA coordinate of the origin point of the map.
• Add tf messages in result.bag so that you can use the result.bag to generate the demo shown above.
• Add rviz_satellate plugins, which can show your point cloud on Google Maps.
• Update map origin point automatically during optimization.

LIO_SAM_6AXIS is compatible with a range of datasets and sensor setups. To help you get started, we have included a table that lists some of the datasets and sensors that have been tested with LIO_SAM_6AXIS.

Dataset	Description	Sensors	Download Links	Ground Truth	Comments
hkust_20201105full		VLP-16, STIM300 IMU, left camera, normal GPS	Dropbox, BaiduNetdisk (password: m8g4)	GT (password:123)	About 10 km outdoor, see this doc
HILTI DATASET		Hesai32 lidar, low-cost IMU, 5 Fisher Eye cameras	Download		The config/params_pandar.yaml is prepared for the HILTI sensors kit
garden_day		Ouster OS1-128, STIM300 IMU, stereo camera	Download	GT	Indoors. When you download this compressed data, remember to execute the following command: rosbag decompress 20220216_garden_day_ref_compressed.bag

• LIO_SAM 6轴IMU适配香港城市数据集UrbanNav，并给出添加GPS约束和不加GPS约束的结果

• integrate LIO-SAM and Imaging_lidar_place_recognition to achieve better mapping and localization result for SLAM system.

If you would like to contribute to LIO_SAM_6AXIS, please read the CONTRIBUTING.md file for guidelines.

LIO_SAM_6AXIS is released under the MIT license.

We would like to thank TixiaoShan for creating the LIO_SAM project that served as the foundation for this work.

We would like to express our gratitude to the following individuals for their contributions to this project:

• Guoqing Zhang for his help with testing and troubleshooting.
• Jianhao Jiao and Jin Wu for his assistance in developing the GPS constraint visualization module.
• Qingwen Zhang for his support with docker support.

We also extend our appreciation to the developers of the LIO_SAM algorithm for providing a solid foundation for our work.

Finally, we would like to thank the open-source community for their contributions to the development and improvement of SLAM technologies, which have made this project possible.