前面把算法在仿真环境都跑通过后，决定拿雷达在真实世界跑一下。先介绍一下最近又发现的两个巨牛的算法：Point-Lio与Faster-lio。

Point-Lio部署

Point-Lio是一种鲁棒且高带宽的LIO算法，具备在极端剧烈运动条件下稳定估计的能力，能够提供准确的、高频的里程计测量（4-8 kHz），可应对严重振动和高角速度或线速度的情况。但对算力的要求较高、CPU负载较大。

先安装livox_ros_driver ，单独创一个工作空间，或者和Point-Lio一个工作空间也行。这里新建一个工作空间：

mkdir -p livox_ros_driver_ws/src #-p  代表递归创建文件夹
cd livox_ros_driver_ws/src
git clone https://github.com/Livox-SDK/livox_ros_driver.git
cd ..
catkin_make

然后安装Point-Lio

mkdir -p Point_Lio_ws/src
cd Point_Lio_ws/src
git clone https://github.com/hku-mars/Point-LIO.git
cd Point-LIO
git submodule update --init
cd ../..

编译前先source一下livox_ros_driver的工作空间（如果point-lio的src下有livox_ros_driver则省去此步骤）

source /home/leo/livox_ros_driver_ws/devel/setup.bash

然后编译

catkin_make

由于官方只对avia等固态激光雷达做了启动文件的适配，并没有对mid360雷达做适配，但我们可以将avia的启动文件中的一些雷达参数改为mid360的参数，主要就是线数、IMU外参这些。以下是我在src/config文件夹下增加的mid360.yaml文件配置：

common:
    lid_topic:  "/livox/lidar" 
    imu_topic:  "/livox/imu" 
    con_frame: false # true: if you need to combine several LiDAR frames into one
    con_frame_num: 1 # the number of frames combined
    cut_frame: false # true: if you need to cut one LiDAR frame into several subframes
    cut_frame_time_interval: 0.1 # should be integral fraction of 1 / LiDAR frequency
    time_lag_imu_to_lidar: 0.0 # Time offset between LiDAR and IMU calibrated by other algorithms, e.g., LI-Init (find in Readme)
                               # the timesample of IMU is transferred from the current timeline to LiDAR's timeline by subtracting this value

preprocess:
    lidar_type: 1 
    scan_line: 4
    timestamp_unit: 1           # the unit of time/t field in the PointCloud2 rostopic: 0-second, 1-milisecond, 2-microsecond, 3-nanosecond.
    blind: 0.5 

mapping:
    imu_en: true
    start_in_aggressive_motion: false # if true, a preknown gravity should be provided in following gravity_init
    extrinsic_est_en: false # for aggressive motion, set this variable false
    imu_time_inte: 0.005 # = 1 / frequency of IMU
    satu_acc: 3.0 # the saturation value of IMU's acceleration. not related to the units
    satu_gyro: 35 # the saturation value of IMU's angular velocity. not related to the units
    acc_norm: 1.0 # 1.0 for g as unit, 9.81 for m/s^2 as unit of the IMU's acceleration
    lidar_meas_cov: 0.001 # 0.001; 0.01
    acc_cov_output: 500
    gyr_cov_output: 1000 
    b_acc_cov: 0.0001 
    b_gyr_cov: 0.0001 
    imu_meas_acc_cov: 0.1 #0.1 # 0.1
    imu_meas_omg_cov: 0.1 #0.01 # 0.1
    gyr_cov_input: 0.01 # for IMU as input model
    acc_cov_input: 0.1 # for IMU as input model
    plane_thr: 0.1 # 0.05, the threshold for plane criteria, the smaller, the flatter a plane
    match_s: 81
    fov_degree: 360 
    det_range: 100
    gravity_align: true # true to align the z axis of world frame with the direction of gravity, and the gravity direction should be specified below
    gravity: [0.0, 0.0, -9.810] # [0.0, 9.810, 0.0] # gravity to be aligned
    gravity_init: [0.0, 0.0, -9.810] # [0.0, 9.810, 0.0] # # preknown gravity in the first IMU body frame, use when imu_en is false or start from a non-stationary state
    extrinsic_T: [ -0.011, -0.02329, 0.04412 ]
    extrinsic_R: [ 1, 0, 0,
                   0, 1, 0,
                   0, 0, 1 ]

odometry: 
    publish_odometry_without_downsample: false

publish:
    path_en: true                 # false: close the path output
    scan_publish_en: true         # false: close all the point cloud output
    scan_bodyframe_pub_en: false  # true: output the point cloud scans in IMU-body-frame

pcd_save:
    pcd_save_en: false
    interval: -1                 # how many LiDAR frames saved in each pcd file; 
                                 # -1 : all frames will be saved in ONE pcd file, may lead to memory crash when having too much frames.

之后在src/launch文件夹下增加mapping_mid360.launch文件：

<launch>
<!-- Launch file for Livox AVIA LiDAR -->

	<arg name="rviz" default="true" />

	<node pkg="point_lio" type="pointlio_mapping" name="laserMapping" output="screen">
	<rosparam command="load" file="$(find point_lio)/config/mid360.yaml" />
	<param name="use_imu_as_input" type="bool" value="1"/> <!--change to 1 to use IMU as input of Point-LIO-->
	<param name="prop_at_freq_of_imu" type="bool" value="1"/>
	<param name="check_satu" type="bool" value="1"/>
	<param name="init_map_size" type="int" value="10"/>
	<param name="point_filter_num" type="int" value="1"/> <!--4, 3--> 
	<param name="space_down_sample" type="bool" value="1" />  
	<param name="filter_size_surf" type="double" value="0.3" /> <!--0.5, 0.3, 0.2, 0.15, 0.1--> 
	<param name="filter_size_map" type="double" value="0.2" /> <!--0.5, 0.3, 0.15, 0.1-->
	<param name="cube_side_length" type="double" value="2000" /> <!--1000-->
	<param name="runtime_pos_log_enable" type="bool" value="0" /> <!--1-->
	</node>
	<group if="$(arg rviz)">
	<node launch-prefix="nice" pkg="rviz" type="rviz" name="rviz" args="-d $(find point_lio)/rviz_cfg/loam_livox.rviz" />
	</group>

	launch-prefix="gdb -ex run --args"

</launch>

然后就可以启动Point-Lio算法了：

source devel/setup.bash
roslaunch point_lio mapping_mid360.launch

Faster-Lio部署

Faster-Lio是一种基于增量体素的激光惯性里程计的方法,是Fast-Lio2 的基础上发表的工作。优点是流程短、计算快，扫描频率高可快速跟踪旋转。缺点也是对算力要求略高。

编译的时候会出现寻找livox_ros_driver驱动的过程，如果之前已经单独安装过，或在其他工作空间安装过，那就耐心等待编译过程寻找就行，不用手动结束编译（实在不行就source一下livox_ros_driver的工作空间）。

mkdir -p faster_lio_ws/src
cd faster_lio_ws/src
git clone https://github.com/gaoxiang12/faster-lio.git
cd ..
catkin_make

同样的在src/config文件夹下增加的mid360.yaml文件配置：

common:
  lid_topic: "/livox/lidar"
  imu_topic: "/livox/imu"
  time_sync_en: false         # ONLY turn on when external time synchronization is really not possible

preprocess:
  lidar_type: 1                # 1 for Livox serials LiDAR, 2 for Velodyne LiDAR, 3 for ouster LiDAR,
  scan_line: 4
  blind: 0.5
  time_scale: 1e-3

mapping:
  acc_cov: 0.1
  gyr_cov: 0.1
  b_acc_cov: 0.0001
  b_gyr_cov: 0.0001
  fov_degree: 360
  det_range: 100.0
  extrinsic_est_en: false      # true: enable the online estimation of IMU-LiDAR extrinsic
  extrinsic_T: [ -0.011, -0.02329, 0.04412  ]
  extrinsic_R: [ 1, 0, 0,
                 0, 1, 0,
                 0, 0, 1 ]

publish:
  path_publish_en: false
  scan_publish_en: true       # false: close all the point cloud output
  scan_effect_pub_en: true    # true: publish the pointscloud of effect point
  dense_publish_en: false       # false: low down the points number in a global-frame point clouds scan.
  scan_bodyframe_pub_en: true  # true: output the point cloud scans in IMU-body-frame

path_save_en: true                 # 保存轨迹，用于精度计算和比较

pcd_save:
  pcd_save_en: true
  interval: -1                 # how many LiDAR frames saved in each pcd file;
  # -1 : all frames will be saved in ONE pcd file, may lead to memory crash when having too much frames.

feature_extract_enable: false
point_filter_num: 3
max_iteration: 3
filter_size_surf: 0.5
filter_size_map: 0.5             # 暂时未用到，代码中为0， 即倾向于将降采样后的scan中的所有点加入map
cube_side_length: 1000

ivox_grid_resolution: 0.5        # default=0.2
ivox_nearby_type: 18             # 6, 18, 26
esti_plane_threshold: 0.1        # default=0.1

在src/launch文件夹下增加mapping_mid360.launch：

<launch>
<!-- Launch file for Livox AVIA LiDAR -->

	<arg name="rviz" default="true" />

	<rosparam command="load" file="$(find faster_lio)/config/mid360.yaml" />

	<param name="feature_extract_enable" type="bool" value="0"/>
	<param name="point_filter_num_" type="int" value="3"/>
	<param name="max_iteration" type="int" value="3" />
	<param name="filter_size_surf" type="double" value="0.5" />
	<param name="filter_size_map" type="double" value="0.5" />
	<param name="cube_side_length" type="double" value="1000" />
	<param name="runtime_pos_log_enable" type="bool" value="1" />
    <node pkg="faster_lio" type="run_mapping_online" name="laserMapping" output="screen" />

	<group if="$(arg rviz)">
	<node launch-prefix="nice" pkg="rviz" type="rviz" name="rviz" args="-d $(find faster_lio)/rviz_cfg/loam_livox.rviz" />
	</group>

</launch>

之后就可以启动Faster-Lio了：

source devel/setup.bash
roslaunch faster_lio mapping_mid360.launch

实物扫描楼层测试

将所有算法移植到NUC上，配置好环境并编译完成后，手持NUC与MID360激光雷达在北京某高层十三楼与十二楼环绕一圈。

最后附上对比视频，从效果上看Faster-Lio的定位建图最准确。