M2DGR数据集在一些SLAM框架上的配置与运行：ORB-SLAM系列、VINS-Mono、LOAM系列、FAST-LIO系列、hdl_graph_slam

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为了说明以上这些SLAM框架的测试与数据集适配，以M2DGR数据集为例进行各自的配置与运行，方便我以后适配自己的数据集，运行的SLAM框架包括：ORB-SLAM2、ORB-SLAM3、VINS-Mono、A-LOAM、LeGO-LOAM、LIO-SAM、LVI-SAM、FAST-LIO2、FAST-LIVO、Faster-LIO、hdl_graph_slam。实验基于Ubuntu20.04，其中ORB、VINS-Mono、和LOAM系列的安装参考我以前的博客，有详细的安装方法，其他的框架在后面会简单介绍一下安装方法

由于学的还不够深入，主要目的就是记录参数配置方法，部分参数配置可能有错误而导致数据跑的有问题，如有发现错误或问题，欢迎讨论，我来修改

一、M2DGR数据集

M2DGR是由具有完整传感器套件的地面机器人收集的新型大规模数据集，传感器套件包括六个鱼眼和一个指向天空的RGB相机、红外相机、事件相机、视觉惯性传感器（VI-传感器）、惯性测量单元（IMU）、LiDAR、消费级全球导航卫星系统（GNSS）接收器和具有实时动态（RTK）信号的GNSS-IMU导航系统。 所有这些传感器都经过了良好的标定和同步，它们的数据被同时记录下来。通过运动捕捉设备、激光3D跟踪器和RTK获得轨迹真值。 该数据集包括36个序列（约1 TB），这些序列是在包括室内和室外环境在内的不同场景中捕获的。

传感器参数如下：

• LIDAR Velodyne VLP-32C, 360 Horizontal Field of View (FOV),-30 to +10 vertical FOV,10Hz,Max Range 200 m,Range Resolution 3 cm, Horizontal Angular Resolution 0.2°.
• RGB Camera FLIR Pointgrey CM3-U3-13Y3C-CS,fish-eye lens,1280*1024,190 HFOV,190 V-FOV, 15 Hz
• GNSS Ublox M8T, GPS/BeiDou, 1Hz
• Infrared Camera,PLUG 617,640*512,90.2 H-FOV,70.6 V-FOV,25Hz;
• V-I Sensor,Realsense d435i,RGB/Depth 640*480,69H-FOV,42.5V-FOV,15Hz;IMU 6-axix, 200Hz
• Event Camera Inivation DVXplorer, 640*480,15Hz;
• IMU,Handsfree A9,9-axis,150Hz;
• GNSS-IMU Xsens Mti 680G. GNSS-RTK,localization precision 2cm,100Hz;IMU 9-axis,100 Hz;
• Laser Scanner Leica MS60, localization 1mm+1.5ppm
• Motion-capture System Vicon Vero 2.2, localization accuracy 1mm, 50 Hz;

后面我们要配置参数，参考标定文件calibration_results.txt，rosbag序列的各数据话题如下：

• LIDAR: /velodyne_points
• RGB Camera:
  /camera/left/image_raw/compressed ,
  /camera/right/image_raw/compressed ,
  /camera/third/image_raw/compressed ,
  /camera/fourth/image_raw/compressed ,
  /camera/fifth/image_raw/compressed ,
  /camera/sixth/image_raw/compressed ,
  /camera/head/image_raw/compressed
• GNSS Ublox M8T:
  /ublox/aidalm ,
  /ublox/aideph ,
  /ublox/fix ,
  /ublox/fix_velocity ,
  /ublox/monhw ,
  /ublox/navclock ,
  /ublox/navpvt ,
  /ublox/navsat ,
  /ublox/navstatus ,
  /ublox/rxmraw
• Infrared Camera:/thermal_image_raw
• V-I Sensor: /camera/color/image_raw/compressed , /camera/imu
• Event Camera: /dvs/events, /dvs_rendering/compressed
• IMU: /handsfree/imu

使用street_02.bag测试各个代码，我们首先通过rqt_bag命令将bag进行可视化，可以看到此数据的时间同步不太好

roscore
rqt_bag street_02.bag

二、ORB-SLAM2

2.1 配置参数

在Examples/Monocular/文件夹中添加参数文件M2DGR.yaml，仿照Examples/Monocular/TUM1.yaml文件修改参数，主要是修改相机内参，其余参数根据需要调整

# Camera calibration and distortion parameters (OpenCV) 
Camera.fx: 617.971050917033
Camera.fy: 616.445131524790
Camera.cx: 327.710279392468
Camera.cy: 253.976983707814

Camera.k1: -0.28340811
Camera.k2: 0.07395907
Camera.p1: 0.00019359
Camera.p2: 1.76187114e-05

Camera.width: 640
Camera.height: 480

# Camera frames per second 
Camera.fps: 15.0

2.2 单目

根据上述图像消息，单目ros_mono.cc文件的订阅:

//ros::Subscriber sub = nodeHandler.subscribe("/camera/image_raw", 1, &ImageGrabber::GrabImage,&igb);
// 替换为：
ros::Subscriber sub = nodeHandler.subscribe("/camera/color/image_raw", 1, &ImageGrabber::GrabImage,&igb);

重新编译ros节点

./build_ros.sh

打开四个终端，分别运行

roscore
#ORB_SLAM2目录下打开终端，运行ORB-SLAM2
rosrun ORB_SLAM2 Mono Vocabulary/ORBvoc.txt Examples/Monocular/M2DGR.yaml
# **注意**：bag中的图像为压缩格式，使用image_transport包将图像解压缩
rosrun image_transport republish compressed in:=/camera/color/image_raw  raw  out:=/camera/color/image_raw
# 在bag所在文件夹下打开终端，开始播放数据集：
rosbag play street_02.bag

纯视觉的问题是初始化慢和特别容易跟踪丢失（运动过快、曝光以及动态物体干扰），另外单目的尺度漂移严重，完全失败

尺度变小挤一疙瘩啦！

三、ORB-SLAM3

3.1 配置参数

在Examples/Monocular-Inertial/文件夹中添加参数文件UrbanNav.yaml，仿照Examples/Monocular-Inertial/EuRoC.yaml文件，根据zed2_intrinsics.yaml、xsens_imu_param.yaml和extrinsic.yaml文件修改参数，主要是修改相机和IMU内参以及联合标定，其余参数根据需要调整

Camera.type: "PinHole"

# Camera calibration and distortion parameters (OpenCV) 
Camera.fx: 617.971050917033
Camera.fy: 616.445131524790
Camera.cx: 327.710279392468
Camera.cy: 253.976983707814

Camera.k1: 0.148000794688248
Camera.k2: -0.217835187249065
Camera.p1: 0.0
Camera.p2: 0.0

Camera.width: 640
Camera.height: 480

# Camera frames per second 
Camera.fps: 15.0

# Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale)
Camera.RGB: 1

# Transformation from camera to body-frame (imu)
Tbc: !!opencv-matrix
   rows: 4
   cols: 4
   dt: f
   data: [0.0,  0.0,  1.0, 0.57711,
           -1.0,  0.0,  0.0, -0.00012,
           0.0,  -1.0,  0.0, 0.83333,
           0.0, 0.0, 0.0, 1.0]

# IMU noise
IMU.NoiseGyro: 2.1309311394972831e-02 #1.6968e-04 
IMU.NoiseAcc: 1.2820343288774358e-01 #2.0e-3
IMU.GyroWalk: 3.6603917782528627e-04
IMU.AccWalk: 1.3677912958097768e-02
IMU.Frequency: 150

3.2 运行单目+IMU

将ros_mono_inertial.cc的IMU与图像消息订阅修改为：

// ros::Subscriber sub_imu = n.subscribe("/imu0", 1000, &ImuGrabber::GrabImu, &imugb); 
// ros::Subscriber sub_img0 = n.subscribe("/cam0/image_raw", 100, &ImageGrabber::GrabImage,&igb);
ros::Subscriber sub_imu = n.subscribe("/handsfree/imu", 1000, &ImuGrabber::GrabImu, &imugb); 
ros::Subscriber sub_img0 = n.subscribe("/camera/color/image_raw", 100, &ImageGrabber::GrabImage,&igb);

编译，然后运行

./build_ros.sh
# 配置环境：
source Examples/ROS
#ORB_SLAM3目录下打开终端，运行ORB-SLAM3
rosrun ORB_SLAM3 Mono_Inertial Vocabulary/ORBvoc.txt Examples/Monocular-Inertial/M2DGR.yaml
# **注意**：bag中的图像为压缩格式，使用image_transport包将图像解压缩
rosrun image_transport republish compressed in:=/camera/color/image_raw  raw  out:=/camera/color/image_raw
# 在bag所在文件夹下打开终端，开始播放数据集：
rosbag play street_02.bag

可以看到漂移十分严重，而且运行过程中也有尺度变化。没有分析出是何原因（猜测可能是/camera/color/image_raw时间戳跳变太严重），后面再作详细分析。总之运行失败

四、VINS-Mono

4.1 配置参数

在config文件夹中添加参数文件M2DGR.yaml，仿照config/euroc/euroc_config.yaml文件，根据my_params_camera.yaml文件修改参数，主要是修改图像以及IMU数据话题、结果输出路径、相机和IMU内参以及联合标定结果，其余参数根据需要调整

#common parameters
imu_topic: "/handsfree/imu"
image_topic: "/camera/color/image_raw"
output_path: "/home/zard/Downloads/VINS-Mono"

# camera model
model_type: PINHOLE
camera_name: camera
image_width: 640
image_height: 480
distortion_parameters:
   k1: 0.148000794688248
   k2: -0.217835187249065
   p1: 0.0
   p2: 0.0
projection_parameters:
   fx: 617.971050917033
   fy: 616.445131524790
   cx: 327.710279392468
   cy: 253.976983707814

#imu parameters       The more accurate parameters you provide, the worse performance
acc_n: 1.2820343288774358e-01          # accelerometer measurement noise standard deviation. #0.2
gyr_n: 2.1309311394972831e-02        # gyroscope measurement noise standard deviation.     #0.05
acc_w: 1.3677912958097768e-02        # accelerometer bias random work noise standard deviation.  #0.02
gyr_w: 3.6603917782528627e-04      # gyroscope bias random work noise standard deviation.     #4.0e-5
g_norm: 9.805       # gravity magnitude

# Extrinsic parameter between IMU and Camera.
estimate_extrinsic: 0   # 0  Have an accurate extrinsic parameters. We will trust the following imu^R_cam, imu^T_cam, don't change it.
                        # 1  Have an initial guess about extrinsic parameters. We will optimize around your initial guess.
                        # 2  Don't know anything about extrinsic parameters. You don't need to give R,T. We will try to calibrate it. Do some rotation movement at beginning. 
#Rotation from camera frame to imu frame, imu^R_cam
extrinsicRotation: !!opencv-matrix
   rows: 3
   cols: 3
   dt: d
   data: [ 0.0,  0.0,  1.0,
           -1.0,  0.0,  0.0, 
           0.0,  -1.0,  0.0]
#Translation from camera frame to imu frame, imu^T_cam
extrinsicTranslation: !!opencv-matrix
   rows: 3
   cols: 1
   dt: d
   data: [0.57711, -0.00012, 0.83333]

仿照vins_estimator/launch/euroc.launch添加vins_estimator/launch/M2DGR.launch启动文件，修改参数文件路径，注意不要忘了将图像解压缩的命令

<arg name="config_path" default = "$(find feature_tracker)/../config/M2DGR.yaml" />


<node pkg="image_transport" type="republish" name="image_republish" args="compressed in:=/camera/color/image_raw raw out:=/camera/color/image_raw" output="screen" respawn="true"/>

4.2 运行单目+IMU

打开三个终端，source后再分别输入

# 运行feature_tracker节点和estimator节点, 订阅图像和IMU数据, 发布位姿, 3D特征点等消息给RVIZ显示
source devel/setup.bash
roslaunch vins_estimator M2DGR.launch
# rviz可视化节点
source devel/setup.bash
roslaunch vins_estimator vins_rviz.launch
# 在bag所在文件夹下打开终端，开始播放数据集：
rosbag play street_02.bag

红色轨迹为前端里程计，绿色轨迹为局部优化和回环结果，此数据没有回环，有较大漂移（猜测也是图像时间戳的问题）

五、DM-VIO

5.1 安装

gtsam、OpenCV和pangolin的安装不赘述，直接编译DM-VIO：

git clone https://github.com/lukasvst/dm-vio.git
cd dm-vio
mkdir build && cd build
cmake ..
make -j8

编译完成后，在build/bin目录下，能看到可执行文件dmvio_dataset，接下来安装ros插件。随便进入一个目录，推荐是刚刚安装的dm-vio的一个子目录下：

git clone https://github.com/lukasvst/dm-vio-ros.git

为了能让这个插件找到刚刚编译的dm-vio.bashrc加上一个环境变量：

sudo gedit ~/.bashrc
# 在最后追加:
export DMVIO_BUILD=/YOURPATH/dm-vio/build

在CMakeLists.txt85行之后添加，不然编译的时候会因为找不到生成的msg格式而报错：

add_dependencies(${PROJECT_NAME}_node ${PROJECT_NAME}_generate_messages_cpp)

编译：

catkin_make
source devel/setup.bash

5.2 配置运行

首先要在config里加上M2DGR的相机模型：

echo -e "RadTan 617.971050917033 616.445131524790 327.710279392468 253.976983707814 0.148000794688248 -0.217835187249065 0.0 0.0\n640 480\nfull\n640 480\n"  > M2DGR.txt

再仿照euroc.yaml配置参数文件M2DGR.yaml

accelerometer_noise_density: 1.2820343288774358e-01
gyroscope_noise_density: 2.1309311394972831e-02
accelerometer_random_walk: 1.3677912958097768e-02
gyroscope_random_walk: 3.6603917782528627e-04
integration_sigma: 0.316227

运行

roscore
source devel/setup.bash
rosrun dmvio_ros node calib=/PATH/M2DGR.txt settingsFile=/PATH/dm-vio/configs/M2DGR.yaml mode=1 nogui=0 preset=1 useimu=1 quiet=1 init_requestFullResetNormalizedErrorThreshold=0.8 init_pgba_skipFirstKFs=1
# **注意**：bag中的图像为压缩格式，使用image_transport包将图像解压缩
rosrun image_transport republish compressed in:=/camera/color/image_raw  raw  out:=/camera/color/image_raw
# 在bag所在文件夹下打开终端，开始播放数据集：
rosbag play street_02.bag /camera/color/image_raw:=cam0/image_raw /handsfree/imu:=imu0

但是运行不出来，没有反应，我不知为何，如果有人知道如何解决请在评论区告诉我，感谢！这里跑一下EuRoC数据吧

echo -e "458.654 457.296 367.215 248.375 -0.28340811 0.07395907 0.00019359 1.76187114e-05\n752 480\ncrop\n640 480\n" > camera.txt

rosrun dmvio_ros node calib=/PATH/camera.txt settingsFile=/PATH/dm-vio/configs/euroc.yaml mode=1 nogui=0 preset=1 useimu=1 quiet=1 init_requestFullResetNormalizedErrorThreshold=0.8 init_pgba_skipFirstKFs=1
rosbag play V1_01_easy.bag

六、A-LOAM

A-LOAM运行很简单，不需特别配置，LiDAR话题为/velodyne_points可直接运行：

source devel/setup.bash
roslaunch aloam_velodyne aloam_velodyne_VLP_32.launch
# 在bag所在文件夹下打开终端，开始播放数据集：
rosbag play street_02.bag

如下图所示，红色轨迹为前端里程计，绿色轨迹为局部优化结果，A-LOAM在此数据集上的效果还是十分好的

侧视图可以看到前端里程计漂移十分严重，说明后端局部优化的重要性

七、LeGO-LOAM

LeGO-LOAM运行也很简单，LiDAR话题为/velodyne_points，IMU话题/handsfree/imu更名为/imu/data：

source devel/setup.bash
roslaunch lego_loam run.launch
# 在bag所在文件夹下打开终端，开始播放数据集：
rosbag play street_02.bag --clock /handsfree/imu:=/imu/data

如下图所示，LeGO-LOAM有较大的漂移

八、LIO-SAM

8.1 配置参数

在config文件夹中添加参数文件config/params_street.yaml，仿照config/params.yaml文件，根xsens_imu_param.yaml和extrinsic.yaml文件修改参数，主要是修改LiDAR以及IMU数据话题、结果输出路径、IMU内参以及联合标定结果，其余参数根据需要调整

pointCloudTopic: "velodyne_points"               # Point cloud data
imuTopic: "/handsfree/imu"                       # IMU data

# Export settings
savePCD: true                              # https://github.com/TixiaoShan/LIO-SAM/issues/3
savePCDDirectory: "～/Output/"        # in your home folder, starts and ends with "/". Warning: the code deletes "LOAM" folder then recreates it. See "mapOptimization" for implementation

# Sensor Settings
sensor: velodyne         # lidar sensor type, either 'velodyne' or 'ouster'
N_SCAN: 32               # number of lidar channel (i.e., 16, 32, 64, 128)
Horizon_SCAN: 1800       # lidar horizontal resolution (Velodyne:1800, Ouster:512,1024,2048)
downsampleRate: 1        # default: 1. Downsample your data if too many points. i.e., 16 = 64 / 4, 16 = 16 / 1
lidarMinRange: 1.0       # default: 1.0, minimum lidar range to be used
lidarMaxRange: 1000.0

# IMU Settings
imuAccNoise: 3.7686306102624571e-02
imuGyrNoise: 2.3417543020438883e-03
imuAccBiasN: 1.1416642385952368e-03
imuGyrBiasN: 1.4428407712885209e-05
# 上海位于北纬31°12′，重力加速度g的精确值为9.7940 m/s²。
imuGravity: 9.7940
imuRPYWeight: 0.01

# Extrinsics (lidar -> IMU)
# 给的是IMU -> lidar，但是旋转矩阵为单位阵，直接取负就行
extrinsicTrans: [0.27255, -0.00053, 0.17954]
extrinsicRot: [1, 0, 0,
               0, 1, 0,
               0, 0, 1]
extrinsicRPY: [1,  0, 0,
               0, 1, 0,
               0, 0, 1]

在launch/run.launch中修改参数文路径

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

8.2 运行

source devel/setup.bash
roslaunch lio_sam run.launch
# 在bag所在文件夹下打开终端，开始播放数据集：
rosbag play street_02.bag

不知为何，LIO-SAM的结果看起来还不如A-LOAM，后面再作分析

九、LVI-SAM

9.1 配置参数

M2DGR的作者提供了LVI-SAM的参数配置文件module_sam.launch、my_params_camera.yaml、my_params_lidar.yaml，主要是要注意以下参数：

# my_params_camera.yaml
#common parameters
imu_topic: "/handsfree/imu"
image_topic: "/camera/color/image_raw"

# lidar to camera extrinsic
lidar_to_cam_tx: 0.27255
lidar_to_cam_ty: -0.00053
lidar_to_cam_tz: 0.17954
lidar_to_cam_rx: 0.0
lidar_to_cam_ry: 0.0
lidar_to_cam_rz: 0.0

# camera model
model_type: PINHOLE
camera_name: camera

# Mono camera config
image_width: 640
image_height: 480
distortion_parameters:
   k1: 0.148000794688248
   k2: -0.217835187249065
   p1: 0
   p2: 0
projection_parameters:
   fx: 617.971050917033
   fy: 616.445131524790
   cx: 327.710279392468
   cy: 253.976983707814

#imu parameters       The more accurate parameters you provide, the worse performance
acc_n: 1.2820343288774358e-01          # accelerometer measurement noise standard deviation. #0.2
gyr_n: 2.1309311394972831e-02        # gyroscope measurement noise standard deviation.     #0.05
acc_w: 1.3677912958097768e-02        # accelerometer bias random work noise standard deviation.  #0.02
gyr_w: 3.6603917782528627e-04      # gyroscope bias random work noise standard deviation.     #4.0e-5
g_norm: 9.805       # gravity magnitude

# Extrinsic parameter between IMU and Camera.
estimate_extrinsic: 0 
extrinsicRotation: !!opencv-matrix
   rows: 3
   cols: 3
   dt: d
   data: [ 0.0,  0.0,  1.0,
           -1.0,  0.0,  0.0, 
           0.0,  -1.0,  0.0]
#Translation from camera frame to imu frame, imu^T_cam
extrinsicTranslation: !!opencv-matrix
   rows: 3
   cols: 1
   dt: d
   data: [0.57711, -0.00012, 0.83333]

#feature traker paprameters
freq: 20

  # my_params_lidar.yaml
  # Topics
  pointCloudTopic: "velodyne_points"               # Point cloud data
  imuTopic: "handsfree/imu"                         # IMU data

  # Heading
  useImuHeadingInitialization: false          # if using GPS data, set to "true"
  
  # Export settings
  savePCD: true                              # https://github.com/TixiaoShan/LIO-SAM/issues/3
  savePCDDirectory: "～/Output/"        # in your home folder, starts and ends with "/". Warning: the code deletes "LOAM" folder then recreates it. See "mapOptimization" for implementation

  # Sensor Settings
  N_SCAN: 32                                  # number of lidar channel (i.e., 16, 32, 64, 128)
  Horizon_SCAN: 1800                          # lidar horizontal resolution (Velodyne:1800, Ouster:512,1024,2048)
  timeField: "time"                           # point timestamp field, Velodyne - "time", Ouster - "t"
  downsampleRate: 1                           # default: 1. Downsample your data if too many points. i.e., 16 = 64 / 4, 16 = 16 / 1 

  # IMU Settings
  imuAccNoise: 3.7686306102624571e-02
  imuGyrNoise: 2.3417543020438883e-03
  imuAccBiasN: 1.1416642385952368e-03
  imuGyrBiasN: 1.4428407712885209e-05
  imuGravity: 9.805
  
  # Extrinsics (lidar -> IMU)
  extrinsicTrans: [0.27255, -0.00053,0.17954]
  extrinsicRot: [1, 0, 0, 0, 1, 0, 0, 0, 1]
  extrinsicRPY: [1, 0, 0, 0, 1, 0, 0, 0, 1]

<node pkg="image_transport" type="republish" name="image_republish" args="compressed in:=/camera/color/image_raw raw out:=/camera/color/image_raw" output="screen" respawn="true"/>
    

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


<param name="vins_config_file" type="string" value="$(find lvi_sam)/config/my_params_camera.yaml" />

9.2 运行

由于M2DGR使用的传感器坐标系与LVI-SAM的数据（实际上是LVI-SAM的IMU比较特殊）不同，M2DGR作者也提供了，需要在内部代码中稍作修改，修改如下：

• src/visual_odometry/visual_estimator/initial/initial_alignment.h

// n(n_in)
// {
//     q_lidar_to_cam = tf::Quaternion(0, 1, 0, 0); 
//     q_lidar_to_cam_eigen = Eigen::Quaterniond(0, 0, 0, 1); 
// }
 n(n_in)
{
    q_lidar_to_cam = tf::Quaternion(0, 0, 0, 1);
    q_lidar_to_cam_eigen = Eigen::Quaterniond(1,0,0,0); 
}

// tf::Quaternion q_odom_cam = tf::createQuaternionFromRPY(0, 0, M_PI) * (q_odom_lidar * q_lidar_to_cam);
tf::Quaternion q_odom_cam = tf::createQuaternionFromRPY(0, 0, M_PI / 2.0) * (q_odom_lidar * q_lidar_to_cam); 

• src/visual_odometry/visual_estimator/utility/visualization.cpp

// tf::Quaternion q_cam_to_lidar(0, 1, 0, 0);
tf::Quaternion q_cam_to_lidar(0, 0, 0, 1); 

// static tf::Transform t_odom_world = tf::Transform(tf::createQuaternionFromRPY(0, 0, M_PI), tf::Vector3(0, 0, 0));
static tf::Transform t_odom_world = tf::Transform(tf::createQuaternionFromRPY(0, 0, 0), tf::Vector3(0, 0, 0));

• src/visual_odometry/visual_feature/feature_tracker_node.cpp**

// 注释以下代码：
// pcl::PointCloud::Ptr laser_cloud_offset(new pcl::PointCloud());
// Eigen::Affine3f transOffset = pcl::getTransformation(L_C_TX, L_C_TY, L_C_TZ, L_C_RX, L_C_RY, L_C_RZ);
// pcl::transformPointCloud(*laser_cloud_in, *laser_cloud_offset, transOffset);
// *laser_cloud_in = *laser_cloud_offset;

修改完后重新编译：

cd ~/catkin_ws
catkin_make -j1  

运行

source devel/setup.bash
roslaunch lvi_sam run.launch
# 在bag所在文件夹下打开终端，开始播放数据集：
rosbag play street_02.bag

与LIO-SAM一样，效果并不理想，看起来甚至不如LIO-SAM

十、LINS

10.1 安装

在已经安装ROS、gtsam和OpenCV的情况下：

cd ~/catkin_ws/src
git clone https://github.com/ChaoqinRobotics/LINS---LiDAR-inertial-SLAM.git
cd ..
catkin_make -j1

注意和LeGO-LOAM一样，第一次编译代码时，需要在“catkin_make”后面加上“-j1”来生成一些消息类型。以后的编译不需要“-j1”，并且如果和LeGO-LOAM放在一个功能包时消息名称会发生冲突（可以选择修改消息名称，也可以删除其中一个消息的编译功能包）

• 由于PCL版本1.10，将C++标准改为14：

#set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -O3")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++14 -O3")

• 遇到错误：error: ‘string’ in namespace ‘std::__cxx11’ does not name a type，将lins/src/lib/parameters.cpp中134和141行的函数参数中的std::__cxx11::string改成std::string
• 四个类似的错误：/usr/bin/ld: cannot find -lBoost::serialization，找不到lBoost的四个库，CMakeLists.txt添加：

find_package(Boost REQUIRED COMPONENTS timer thread serialization chrono)

10.2 配置参数

acc_n: 3.7686306102624571e-02
gyr_n: 2.3417543020438883e-03
acc_w: 1.1416642385952368e-03
gyr_w: 1.4428407712885209e-05
# extrinsic parameters
init_tbl: !!opencv-matrix
   rows: 3
   cols: 1
   dt: d
   data: [0.27255, -0.00053,0.17954]
   
init_rbl: !!opencv-matrix
   rows: 3
   cols: 3
   dt: d
   data:  [1, 0, 0, 
           0, 1, 0, 
           0, 0, 1]

<arg name="config_path" default = "$(find lins)/config/exp_config/M2DGR.yaml" />

10.3 运行

source devel/setup.bash
roslaunch lins run_M2DGR.launch
# 在bag所在文件夹下打开终端，开始播放数据集：
rosbag play street_02.bag --clock /handsfree/imu:=/imu/data

运行时报错：[ERROR] [1677898050.385013867, 1627957054.444385592]: Error transforming odometry 'Odometry' from frame '/camera_init' to frame 'map'，在lidar_mapping_node.cpp、transform_fusion_node.cpp、Estimator.cpp代码里搜索所有的/camera_init改成camera_init
竖直方向上漂移较大

十一、FAST-LIO2

FAST-LIO是香港大学火星实验室推出的高效 LiDAR 惯性里程计框架，使用紧密耦合的迭代扩展卡尔曼滤波器将 LiDAR 特征点与 IMU 数据融合，以允许在发生退化的快速运动、嘈杂或杂乱环境中进行稳健导航。相较第一代版本，第二代使用ikd-Tree增量映射，实现支持超过 100Hz 的 LiDAR 速率，并且支持更多的LiDAR和设备

11.1 安装

不需要修改源码，首先安装依赖项：

• ROS (melodic or noetic)，安装方法参考我之前的博客
• glog: sudo apt-get install libgoogle-glog-dev
• eigen: sudo apt-get install libeigen3-dev
• pcl: sudo apt-get install libpcl-dev
• yaml-cpp: sudo apt-get install libyaml-cpp-dev

（1）Livox-sdk安装

git clone https://github.com/Livox-SDK/Livox-SDK.git
cd Livox-SDK
cd build && cmake ..
make
sudo make install

（2）Livox_ros_driver安装

# 打开终端下执行下面的内容
mkdir ws_livox/src -p
# 打开配置文件
gedit ~/.bashrc
# 将下面的内容添加到文件最后
source ~/ws_livox/devel/setup.bash
# 将livox_ros_driver clone 到 ws_livox/src 下
cd ~/ws_livox/src
git clone https://github.com/Livox-SDK/livox_ros_driver.git
cd ..
catkin_make
source devel/setup.sh

（3）FAST_LIO安装

cd ~/ws_livox/src
git clone https://github.com/hku-mars/FAST_LIO.git
cd ..
catkin_make

11.2 配置参数

同理：

common:
    lid_topic:  "/velodyne_points"
    imu_topic:  "/handsfree/imu"
    
preprocess:
    lidar_type: 2                # 1 for Livox serials LiDAR, 2 for Velodyne LiDAR, 3 for ouster LiDAR, 
    scan_line: 32
    scan_rate: 10                # only need to be set for velodyne, unit: Hz,
    timestamp_unit: 2            # the unit of time/t field in the PointCloud2 rostopic: 0-second, 1-milisecond, 2-microsecond, 3-nanosecond.
    blind: 2

mapping:
    acc_cov: 3.7686306102624571e-02
    gyr_cov: 2.3417543020438883e-03
    b_acc_cov: 1.1416642385952368e-03
    b_gyr_cov: 1.4428407712885209e-05
    extrinsic_est_en:  false      # true: enable the online estimation of IMU-LiDAR extrinsic,
    extrinsic_T: [ 0.27255, -0.00053,0.17954]
    extrinsic_R: [ 1, 0, 0, 
                   0, 1, 0, 
                   0, 0, 1]
publish:
	# 发布路径
    path_en: true

pcd_save:
	# 如无必要不用保存点云地图，太大了
    pcd_save_en: false

<rosparam command="load" file="$(find fast_lio)/config/velodyne_M2DGR.yaml" />

11.3 运行

source devel/setup.bash
roslaunch fast_lio mapping_velodyne_M2DGR.launch
# 在bag所在文件夹下打开终端，开始播放数据集：
rosbag play street_02.bag

看起来还行

十二、FAST-LIVO

12.1 安装

（1）已安装：

• Ubuntu 16.04~20.04. ROS Installation.
• PCL>=1.6, Follow PCL Installation.
• Eigen>=3.3.4, Follow Eigen Installation.
• OpenCV>=3.2, Follow Opencv Installation.
• Livox-sdk
• Livox_ros_driver

（2）安装非模板类的Sophus：

git clone https://github.com/strasdat/Sophus.git
cd Sophus
# 版本回溯
git checkout a621ff
mkdir build && cd build
cmake ..
make

在make之前，先修改这个版本Sophus的bug的，打开Sophus/sophus/so2.cpp文件，将代码修改如下

//   unit_complex_.real() = 1.;
//   unit_complex_.imag() = 0.;
unit_complex_.real(1.); 
unit_complex_.imag(0.); 

make
# 如果已经安装过模板类的不用担心，安装时互不影响,*.h和*.hpp可以共存于一个文件夹
sudo make install

（3）安装Vikit

cd ~/catkin_ws/src
git clone https://github.com/uzh-rpg/rpg_vikit.git

（4）安装FAST-LIVO

cd ~/catkin_ws/src
git clone https://github.com/hku-mars/FAST-LIVO
cd ..
catkin_make

如果报OpenCV的错，将CMakeLists.txt中的

FIND_PACKAGE(OpenCV REQUIRED)
# 改为
FIND_PACKAGE(OpenCV 4 REQUIRED)

将报错的参数替换

// CV_RANSAC
cv::RANSAC
// CV_INTER_LINEAR   //双线性插值
cv::INTER_LINEAR
// CV_WINDOW_AUTOSIZE
cv::WINDOW_AUTOSIZE

如果出现大量的类似错误：/usr/bin/ld: /home/zard/catkin_ws_LiDAR/devel/lib/libvikit_common.so: undefined reference to Sophus::SE3::operator*(Eigen::Matrix const&) const，执行FIND_PACKAGE(sophus REQUIRED)时，libSophus.so 应该被链接到 Sophus_LIBRARIES， 但cmake却没链接上（原因未知），因此出现这个错误。显式将Sophus_LIBRARIES 链接到libSophus.so：

FIND_PACKAGE(Sophus REQUIRED)
set(Sophus_LIBRARIES libSophus.so)

12.2 运行

写到这里发现由于FAST-LIVO需要严格的时间同步，M2DGR数据不太行，但是写了就不删了，这里运行示例数据：

roslaunch fast_livo mapping_avia.launch
rosbag play hk1.bag

图建的十分漂亮，也很稳

12.3 R3LIVE

这里我补充一下类似的，R3LIVE的编译运行，已安装Livox-sdk、Livox_ros_driver
（1）CGAL

sudo apt-get install libcgal-dev

（2）编译

cd ~/catkin_ws/src
git clone https://github.com/hku-mars/r3live.git
catkin_make
source ~/catkin_ws/devel/setup.bash

（3）运行

roslaunch r3live r3live_bag.launch
rosbag play YOUR.bag

十三、Faster-LIO

Faster_LIO是高博在FAST_LIO上改进的开源SLAM，使得SLAM的效率更高。

13.1 Faster-LIO的安装

Faster-LIO的编译十分简单，高博已经在20.04测试过了，首先安装依赖项：

• ROS (melodic or noetic)，安装方法参考我之前的博客
• glog: sudo apt-get install libgoogle-glog-dev
• eigen: sudo apt-get install libeigen3-dev
• pcl: sudo apt-get install libpcl-dev
• yaml-cpp: sudo apt-get install libyaml-cpp-dev

之后可以编译：

# 注意不要和Livox_ros_driver在同一个工作空间中，否则Livox_ros_driver节点名称冲突
mkdir catkin_ws/src -p
cd catkin_ws
catkin_make

13.2 配置参数

添加配置文件以及launch文件，主要修改以下内容

common:
    dataset: "M2DGR"
    lid_topic:  "velodyne_points"
    imu_topic:  "handsfree/imu"
    time_sync_en: false         # ONLY turn on when external time synchronization is really not possible
    
preprocess:
    lidar_type: 2                # 1 for Livox serials LiDAR, 2 for Velodyne LiDAR, 3 for ouster LiDAR, 
    scan_line: 32
    blind: 4
    time_scale: 1e3             # 兼容不同数据集的时间单位

mapping:
    acc_cov: 3.7686306102624571e-02
    gyr_cov: 2.3417543020438883e-03
    b_acc_cov: 1.1416642385952368e-03
    b_gyr_cov: 1.4428407712885209e-05
    fov_degree:    180
    det_range:     100.0
    extrinsic_est_en:  true      # true: enable the online estimation of IMU-LiDAR extrinsic
    extrinsic_T: [ 0.27255, -0.00053,0.17954]
    extrinsic_R: [ 1, 0, 0, 
                   0, 1, 0, 
                   0, 0, 1]

<rosparam command="load" file="$(find faster_lio)/config/velodyne_M2DGR.yaml" />

13.3 运行

source devel/setup.bash
roslaunch faster_lio mapping_velodyne_M2DGR.launch
# 在bag所在文件夹下打开终端，开始播放数据集：
rosbag play street_02.bag

Faster-Lio效果在视觉上看着出奇的好，建的图细节上很漂亮。具体精度要看后面比较，不过运行效率亲测确实比FAST-LIO高很多

十四、hdl_graph_slam

hdl-graph-slam不仅融合了imu、gps、平面、激光等多种输入，同时也带闭环检测等后端处理，即完整的一个slam架构。

14.1 安装

首先我们安装了ROS，然后

sudo apt-get install ros-noetic-geodesy ros-noetic-pcl-ros ros-noetic-nmea-msgs ros-noetic-libg2o

cd catkin_ws/src
git clone https://github.com/koide3/ndt_omp.git
git clone https://github.com/SMRT-AIST/fast_gicp.git --recursive
git clone https://github.com/koide3/hdl_graph_slam

cd .. && catkin_make -DCMAKE_BUILD_TYPE=Release

14.2 纯激光

打开四个终端，分别

# 启动master节点
roscore
# 设置use_sim_time参数并运行
rosparam set use_sim_time true
roslaunch hdl_graph_slam hdl_graph_slam_400.launch
# 启动rviz可视化
roscd hdl_graph_slam/rviz
rviz -d hdl_graph_slam.rviz
# 在bag所在文件夹下打开终端，开始播放数据集：
rosbag play street_02.bag --clock

十五、定位结果质量评估与比较

至于轨迹的保存，我都是在建图模块添加保存TUM格式位姿的代码，这里就不详细写了
首先求得每个轨迹的绝对误差（A-LOAM为例，street_02.txt为轨迹真值，A-LOAM为A-LOAM轨迹输出结果）：

evo_ape tum street_02.txt A-LOAM -va -s --plot --plot_mode=xy --save_results A-LOAM.zip

同样的方法求得其他轨迹的绝对轨迹误差结果，放在同一个文件夹下，比较：

可以看到ORB-SLAM3的误差太大了，我们去掉它比较其他的

对于此数据，视觉效果不太好，猜测可能是图像时间戳跳变严重并且频率较低。而其他传感器的数据在各个算法的效果也都平平，把各个算法都拉到同一个水平线上，大多数多源融合算法甚至不如纯激光的A-LOAM，哈哈哈哈哈哈哈