LIO_SAM_6AXIS

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.

Features

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.

Getting Started

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:

# set your bag_path here
roslaunch lio_sam_6axis test_vlp16.launch

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

4. finally, save your point cloud map.

# map is in the LIO-SAM-6AXIS/data 
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

Documentation

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

Latest News(2024-04-25)

Here are the latest updates to LIO_SAM_6AXIS:

• Add support for Velodyne Velarray M1600 #75 from pedrotomas27

Dataset and Adaptation

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 2022		Hesai32 lidar, low-cost IMU, 5 Fisher Eye cameras	Download		The config/params_pandar.yaml is prepared for the HILTI sensors kit
FusionPortable DATASET		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
Multimodal Dataset		MS1600	Download		Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol Particles for Frontier Exploration

Related Package

1. LIO-SAM-6AXIS-UrbanNav

• 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

2. LIO-SAM-6AXIS-INTENSITY

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

Credits

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

Acknowledgments

Our deep gratitude goes to pedrotomas27, Guoqing Zhang, Jianhao Jiao, Jin Wu, and Qingwen Zhang for their invaluable contributions to this project. A special mention goes to the LIO_SAM for laying the groundwork for our efforts. We also thank the open-source community, whose relentless pursuit of SLAM technology advancement has made this project possible.