LIO-SAM-master.zip

# LIO-SAM **A real-time lidar-inertial odometry package. We strongly recommend the users read this document thoroughly and test the package with the provided dataset first. A video of the demonstration of the method can be found on [YouTube](https://www.youtube.com/watch?v=A0H8CoORZJU).** <p align='center'> <img src="./config/doc/demo.gif" alt="drawing" width="800"/> </p> <p align='center'> <img src="./config/doc/device-hand-2.png" alt="drawing" width="200"/> <img src="./config/doc/device-hand.png" alt="drawing" width="200"/> <img src="./config/doc/device-jackal.png" alt="drawing" width="200"/> <img src="./config/doc/device-boat.png" alt="drawing" width="200"/> </p> ## Menu - [**System architecture**](#system-architecture) - [**Package dependency**](#dependency) - [**Package install**](#install) - [**Prepare lidar data**](#prepare-lidar-data) (must read) - [**Prepare IMU data**](#prepare-imu-data) (must read) - [**Sample datasets**](#sample-datasets) - [**Run the package**](#run-the-package) - [**Other notes**](#other-notes) - [**Issues**](#issues) - [**Paper**](#paper) - [**TODO**](#todo) - [**Related Package**](#related-package) - [**Acknowledgement**](#acknowledgement) ## System architecture <p align='center'> <img src="./config/doc/system.png" alt="drawing" width="800"/> </p> We design a system that maintains two graphs and runs up to 10x faster than real-time. - The factor graph in "mapOptimization.cpp" optimizes lidar odometry factor and GPS factor. This factor graph is maintained consistently throughout the whole test. - The factor graph in "imuPreintegration.cpp" optimizes IMU and lidar odometry factor and estimates IMU bias. This factor graph is reset periodically and guarantees real-time odometry estimation at IMU frequency. ## Dependency This is the original ROS1 implementation of LIO-SAM. For a ROS2 implementation see branch `ros2`. - [ROS](http://wiki.ros.org/ROS/Installation) (tested with Kinetic and Melodic) ``` sudo apt-get install -y ros-kinetic-navigation sudo apt-get install -y ros-kinetic-robot-localization sudo apt-get install -y ros-kinetic-robot-state-publisher ``` - [gtsam](https://github.com/borglab/gtsam/releases) (Georgia Tech Smoothing and Mapping library) ``` wget -O ~/Downloads/gtsam.zip https://github.com/borglab/gtsam/archive/4.0.2.zip cd ~/Downloads/ && unzip gtsam.zip -d ~/Downloads/ cd ~/Downloads/gtsam-4.0.2/ mkdir build && cd build cmake -DGTSAM_BUILD_WITH_MARCH_NATIVE=OFF .. sudo make install -j8 ``` ## Install Use the following commands to download and compile the package. ``` cd ~/catkin_ws/src git clone https://github.com/TixiaoShan/LIO-SAM.git cd .. catkin_make ``` ## Prepare lidar data The user needs to prepare the point cloud data in the correct format for cloud deskewing, which is mainly done in "imageProjection.cpp". The two requirements are: - **Provide point time stamp**. LIO-SAM uses IMU data to perform point cloud deskew. Thus, the relative point time in a scan needs to be known. The up-to-date Velodyne ROS driver should output this information directly. Here, we assume the point time channel is called "time." The definition of the point type is located at the top of the "imageProjection.cpp." "deskewPoint()" function utilizes this relative time to obtain the transformation of this point relative to the beginning of the scan. When the lidar rotates at 10Hz, the timestamp of a point should vary between 0 and 0.1 seconds. If you are using other lidar sensors, you may need to change the name of this time channel and make sure that it is the relative time in a scan. - **Provide point ring number**. LIO-SAM uses this information to organize the point correctly in a matrix. The ring number indicates which channel of the sensor that this point belongs to. The definition of the point type is located at the top of "imageProjection.cpp." The up-to-date Velodyne ROS driver should output this information directly. Again, if you are using other lidar sensors, you may need to rename this information. Note that only mechanical lidars are supported by the package currently. ## Prepare IMU data - **IMU requirement**. Like the original LOAM implementation, LIO-SAM only works with a 9-axis IMU, which gives roll, pitch, and yaw estimation. The roll and pitch estimation is mainly used to initialize the system at the correct attitude. The yaw estimation initializes the system at the right heading when using GPS data. Theoretically, an initialization procedure like VINS-Mono will enable LIO-SAM to work with a 6-axis IMU. The performance of the system largely depends on the quality of the IMU measurements. The higher the IMU data rate, the better the system accuracy. We use Microstrain 3DM-GX5-25, which outputs data at 500Hz. We recommend using an IMU that gives at least a 200Hz output rate. Note that the internal IMU of Ouster lidar is an 6-axis IMU. - **IMU alignment**. LIO-SAM transforms IMU raw data from the IMU frame to the Lidar frame, which follows the ROS REP-105 convention (x - forward, y - left, z - upward). To make the system function properly, the correct extrinsic transformation needs to be provided in "params.yaml" file. **The reason why there are two extrinsics is that my IMU (Microstrain 3DM-GX5-25) acceleration and attitude have different cooridinates. Depend on your IMU manufacturer, the two extrinsics for your IMU may or may not be the same**. Using our setup as an example: - we need to set the readings of x-z acceleration and gyro negative to transform the IMU data in the lidar frame, which is indicated by "extrinsicRot" in "params.yaml." - The transformation of attitude readings is slightly different. We rotate the attitude measurements by -90 degrees around "lidar-z" axis and get the corresponding roll, pitch, and yaw readings in the lidar frame. This transformation is indicated by "extrinsicRPY" in "params.yaml." - **IMU debug**. It's strongly recommended that the user uncomment the debug lines in "imuHandler()" of "imageProjection.cpp" and test the output of the transformed IMU data. The user can rotate the sensor suite to check whether the readings correspond to the sensor's movement. A YouTube video that shows the corrected IMU data can be found [here (link to YouTube)](https://youtu.be/BOUK8LYQhHs). <p align='center'> <img src="./config/doc/imu-transform.png" alt="drawing" width="800"/> </p> <p align='center'> <img src="./config/doc/imu-debug.gif" alt="drawing" width="800"/> </p> ## Sample datasets * Download some sample datasets to test the functionality of the package. The datasets below is configured to run using the default settings: - **Walking dataset:** [[Google Drive](https://drive.google.com/drive/folders/1gJHwfdHCRdjP7vuT556pv8atqrCJPbUq?usp=sharing)] - **Park dataset:** [[Google Drive](https://drive.google.com/drive/folders/1gJHwfdHCRdjP7vuT556pv8atqrCJPbUq?usp=sharing)] - **Garden dataset:** [[Google Drive](https://drive.google.com/drive/folders/1gJHwfdHCRdjP7vuT556pv8atqrCJPbUq?usp=sharing)] * The datasets below need the parameters to be configured. In these datasets, the point cloud topic is "points_raw." The IMU topic is "imu_correct," which gives the IMU data in ROS REP105 standard. Because no IMU transformation is needed for this dataset, the following configurations need to be changed to run this dataset successfully: - The "imuTopic" parameter in "config/params.yaml" needs to be set to "imu_correct". - The "extrinsicRot" and "extrinsicRPY" in "config/params.yaml" needs to be set as identity matrices. - **Rotation dataset:** [[Google Drive](https://drive.google.com/drive/folders/1gJHwfdHCRdjP7vuT556pv8atqrCJPbUq?usp=sharing)] - **Campus dataset (large):** [[Google Drive](https://drive.google.com/drive/folders/1gJHwfdHCRdjP7vuT556pv8atqrCJPbUq?usp=sharing)] - **Campus dataset (small):** [[Google