最近一直有3d激光建图的需求，选择比较火的lio-sam为例，采集自己的数据集
一、跑开源的数据集
详细的可以参考：https://blog.youkuaiyun.com/unlimitedai/article/details/107378759
二、跑自己录的数据集
配置：
速腾16线激光雷达: RS-LiDAR-16
修改后的launch文件run_gugao.launch：
<launch>
<arg name="project" default="lio_sam"/>
<!-- Parameters -->
<rosparam file="$(find lio_sam)/config/params_mingnuo.yaml" command="load" />
<!--- LOAM -->
<include file="$(find lio_sam)/launch/include/module_loam.launch" />
<!--- Robot State TF -->
<include file="$(find lio_sam)/launch/include/module_robot_state_publisher.launch" />
<!--- Run Navsat -->
<include file="$(find lio_sam)/launch/include/module_navsat.launch" />
<!--- Run Rviz-->
<include file="$(find lio_sam)/launch/include/module_rviz.launch" />
</launch>
修改后的config文件：
lio_sam:
# Topics
pointCloudTopic: "rslidar_points" # Point cloud data
imuTopic: "imu_data" # IMU data
odomTopic: "odometry/imu" # IMU pre-preintegration odometry, same frequency as IMU
gpsTopic: "odometry/gpsz" # GPS odometry topic from navsat, see module_navsat.launch file
#gpsTopic: "aaa"
# Frames
lidarFrame: "base_link"
baselinkFrame: "base_link"
odometryFrame: "odom"
mapFrame: "map"
# GPS Settings
useImuHeadingInitialization: true # if using GPS data, set to "true"
useGpsElevation: false # if GPS elevation is bad, set to "false"
gpsCovThreshold: 2.0 # m^2, threshold for using GPS data
poseCovThreshold: 25.0 # m^2, threshold for using GPS data
# Export settings
savePCD: true # https://github.com/TixiaoShan/LIO-SAM/issues/3
savePCDDirectory: "/houduan/LIO-SAM_down/LIO-SAM_ws/out1/" # 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: 16 # 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 # default: 1000.0, maximum lidar range to be used
# IMU Settings
imuAccNoise: 3.9939570888238808e-03
imuGyrNoise: 1.5636343949698187e-03
imuAccBiasN: 6.4356659353532566e-05
imuGyrBiasN: 3.5640318696367613e-05
imuGravity: 9.80511
imuRPYWeight: 0.01
# Extrinsics (lidar -> IMU)
extrinsicTrans: [0.0, 0.0, 0.0]
#extrinsicRot: [-1, 0, 0,
# 0, 1, 0,
# 0, 0, -1]
#extrinsicRPY: [0, 1, 0,
# -1, 0, 0,
# 0, 0, 1]
extrinsicRot: [1, 0, 0,
0, 1, 0,
0, 0, 1]
extrinsicRPY: [1, 0, 0,
0, 1, 0,
0, 0, 1]
# LOAM feature threshold
edgeThreshold: 1.0
surfThreshold: 0.1
edgeFeatureMinValidNum: 10
surfFeatureMinValidNum: 100
# voxel filter paprams
odometrySurfLeafSize: 0.4 # default: 0.4 - outdoor, 0.2 - indoor
mappingCornerLeafSize: 0.2 # default: 0.2 - outdoor, 0.1 - indoor
mappingSurfLeafSize: 0.4 # default: 0.4 - outdoor, 0.2 - indoor
# robot motion constraint (in case you are using a 2D robot)
z_tollerance: 1000 # meters
rotation_tollerance: 1000 # radians
# CPU Params
numberOfCores: 4 # number of cores for mapping optimization
mappingProcessInterval: 0.15 # seconds, regulate mapping frequency
# Surrounding map
surroundingkeyframeAddingDistThreshold: 1.0 # meters, regulate keyframe adding threshold
surroundingkeyframeAddingAngleThreshold: 0.2 # radians, regulate keyframe adding threshold
surroundingKeyframeDensity: 2.0 # meters, downsample surrounding keyframe poses
surroundingKeyframeSearchRadius: 50.0 # meters, within n meters scan-to-map optimization (when loop closure disabled)
# Loop closure
loopClosureEnableFlag: true
loopClosureFrequency: 1.0 # Hz, regulate loop closure constraint add frequency
surroundingKeyframeSize: 50 # submap size (when loop closure enabled)
historyKeyframeSearchRadius: 15.0 # meters, key frame that is within n meters from current pose will be considerd for loop closure
historyKeyframeSearchTimeDiff: 30.0 # seconds, key frame that is n seconds older will be considered for loop closure
historyKeyframeSearchNum: 25 # number of hostory key frames will be fused into a submap for loop closure
historyKeyframeFitnessScore: 0.3 # icp threshold, the smaller the better alignment
# Visualization
globalMapVisualizationSearchRadius: 1000.0 # meters, global map visualization radius
globalMapVisualizationPoseDensity: 10.0 # meters, global map visualization keyframe density
globalMapVisualizationLeafSize: 1.0 # meters, global map visualization cloud density
# Navsat (convert GPS coordinates to Cartesian)
navsat:
frequency: 50
wait_for_datum: false
delay: 0.0
magnetic_declination_radians: 0
yaw_offset: 0
zero_altitude: true
broadcast_utm_transform: false
broadcast_utm_transform_as_parent_frame: false
publish_filtered_gps: false
# EKF for Navsat
ekf_gps:
publish_tf: false
map_frame: map
odom_frame: odom
base_link_frame: base_link
world_frame: odom
frequency: 50
two_d_mode: false
sensor_timeout: 0.01
# -------------------------------------
# External IMU:
# -------------------------------------
imu0: imu_correct
# make sure the input is aligned with ROS REP105. "imu_correct" is manually transformed by myself. EKF can also transform the data using tf between your imu and base_link
imu0_config: [false, false, false,
true, true, true,
false, false, false,
false, false, true,
true, true, true]
imu0_differential: false
imu0_queue_size: 50
imu0_remove_gravitational_acceleration: true
# -------------------------------------
# Odometry (From Navsat):
# -------------------------------------
odom0: odometry/gps
odom0_config: [true, true, true,
false, false, false,
false, false, false,
false, false, false,
false, false, false]
odom0_differential: false
odom0_queue_size: 10
# x y z r p y x_dot y_dot z_dot r_dot p_dot y_dot x_ddot y_ddot z_ddot
process_noise_covariance: [ 1.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 10.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0.03, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0.03, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0.25, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0.25, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0.04, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.015]
修改点：
imu话题
激光话题
imu和激光外参extrinsicRot和extrinsicRPY
保存地图的开关：
savePCD: true # https://github.com/TixiaoShan/LIO-SAM/issues/3
savePCDDirectory: “/houduan/LIO-SAM_down/LIO-SAM_ws/out1/” # 记住不要加/home/name
建图过程出乎意料的顺利，而且最后效果很好！
如下图：
程序运行后的tf如下：
节点间的话题调用关系：
三、lio-sam算法学习
3.1、系统如何初始化
lio-sam系统初始定位信息来自于imu的积分结果，/odometry/imu_incremental话题在imageProjection进程里面被写入到cloudInfo中：
cloudInfo.initialGuessX = startOdomMsg.pose.pose.position.x;
cloudInfo.initialGuessY = startOdomMsg.pose.pose.position.y;
cloudInfo.initialGuessZ = startOdomMsg.pose.pose.position.z;
cloudInfo.initialGuessRoll = roll;
cloudInfo.initialGuessPitch = pitch;
cloudInfo.initialGuessYaw = yaw;
cloudInfo.odomAvailable = true;
将imu里程计的位姿记录，发布出去用于地图优化的初始值
3.2、系统运行后
lio-sam系统运行后严格依赖imu积分结果/odometry/imu_incremental，我通过vscode工具，暂停住mapOptimization进程，只让imu进行积分，过几分钟后继续跑mapOptimization进程，最后得到的/lio_sam/mapping/path会跳到imu积分的结果/lio_sam/imu/path处。如下图：
四、定位模式
4.1 网上开源定位模式代码
网上有个小哥(应该是哈工大的那波人，都写的是HIT)，在lio_sam的基础上增加了重定位，感觉还可以用，比一波粒子滤波的开源代码强一些。最起码定位频率和精度还跟得上，缺点是重定位不准，慢，可能还需要详细看代码调试，真实测试应该很快同时定位准确。
地址:https://github.com/Gaochao-hit/LIO-SAM_based_relocalization
来2张导航图
**总结：**这份开源代码实现了最原始的定位模式功能。但是感觉比较粗糙，而且只使用了cloudGlobal.pcd，特征和trajectory.pcd没有利用起来。后续可以基于此进行修改。
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