Using the Sagitar Radar 32-line Helios and fast-LIO2 algorithm to build a map (3)

1. Mapping

1. Source code download

https://github.com/hku-mars/FAST_LIO

2. Configure the environment

Step 1: Install PCL >= 1.8, PCL installation can refer to
https://blog.csdn.net/qq_33328642/article/details/119186766

sudo apt-get update  
sudo apt-get install git build-essential linux-libc-dev
sudo apt-get install cmake cmake-gui
sudo apt-get install libusb-1.0-0-dev libusb-dev libudev-dev
sudo apt-get install mpi-default-dev openmpi-bin openmpi-common 
sudo apt-get install libflann1.9 libflann-dev 
sudo apt-get install libeigen3-dev 
sudo apt-get install libboost-all-dev 

1 sudo apt-get install libvtk7.1-qt libvtk7.1 libvtk7-qt-dev  
--如果上面1报错，先运行下面指令
sudo apt-get install libvtk7-jni
--再重新运行,如果1正常则不需要运行sudo apt-get install libvtk7-jni

sudo apt-get install libqhull* libgtest-dev
sudo apt-get install freeglut3-dev pkg-config
sudo apt-get install libxmu-dev libxi-dev
sudo apt-get install mono-complete
sudo apt-get install openjdk-8-jdk openjdk-8-jre

Download pcl1.8
https://github.com/Kyle-ak/pcl/releases/tag/pcl-1.8.0
for long time compilation

cd pcl-1.8
mkdir build && cd build
cmake -DCMAKE_BUILD_TYPE=None -DCMAKE_INSTALL_PREFIX=/usr \ -DBUILD_GPU=ON-DBUILD_apps=ON -DBUILD_examples=ON \ -DCMAKE_INSTALL_PREFIX=/usr .. 
make -j8
sudo make install 

Step 2: Eigen >= 3.3.4 ,

--下载路径
https://github.com/eigenteam/eigen-git-mirror/archive/3.3.7.tar.gz   true
--编译
cd eigen-git-mirror
mkdir build
cd build
cmake ..
sudo make install
-- 添加链接
sudo cp -r /usr/local/include/eigen3 /usr/include/eigen3
sudo ln -s /usr/include/eigen3/Eigen /usr/include/Eigen

3. Compile the fast-lio package

When using git to download the source code, when git clone is not used, the ikd-Tree folder in include is empty, because the source code git provides a git link, which can be downloaded by yourself. A major innovation of fast-lio2 is the use of ikd-Tree. to maintain a large number of map points.
The first step: download link fast-lio
https://github.com/hku-mars/FAST_LIO
download link kdtree link
https://github.com/hku-mars/ikd-Tree

Step 2: Put the downloaded complete fast-lio in the created workspace catkin_ws/src path, run

catkin_make 

For some library problems, please refer to https://blog.csdn.net/qin__han/article/details/123409914 to solve

Step 3: Configuration file:
1. Modify the FAST_LIO\src\FAST_LIO\config\velodyne.yaml
topic corresponding settings

common:
    lid_topic:  "/velodyne_points"
    imu_topic:  "/imu/data"

Lidar configuration

preprocess:
    lidar_type: 2      # 1 表示Livox 雷达, 2 表示Velodyne 雷达, 3 表示ouster 雷达
    scan_line: 32      # 32线
    scan_rate: 10      #   velodyne 频率, unit: Hz,

IMU and lidar are relatively external parameters,

extrinsic_T: [ 0, 0, 0]
extrinsic_R: [ 1, 0, 0, 
               0, 1, 0, 
               0, 0, 1]
# 我的参数是因为IMU在lidar的正上方位置