lio-sam建图实现

本文主要是介绍lio-sam建图实现，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

参考：https://blog.csdn.net/unlimitedai/article/details/107378759

https://blog.csdn.net/weixin_44126988/article/details/131654142?ops_request_misc=&request_id=&biz_id=102&utm_term=lego-loam%E5%BB%BA%E5%9B%BE%E8%BD%AC%E4%B8%BA%E6%A0%85%E6%A0%BC%E5%9C%B0%E5%9B%BE&utm_medium=distribute.pc_search_result.none-task-blog-2_allsobaiduweb~default-0-131654142.nonecase&spm=1018.2226.3001.4187

https://blog.csdn.net/zhuchao414259/article/details/127993112?ops_request_misc=&request_id=&biz_id=102&utm_term=rslidar3d%E5%BB%BA%E5%9B%BE&utm_medium=distribute.pc_search_result.none-task-blog-2_allsobaiduweb~default-0-127993112.142^v100control&spm=1018.2226.3001.4187
代码：https://github.com/TixiaoShan/LIO-SAM
数据集： https://pan.baidu.com/s/1-sAB_cNlYPqTjDuaFgz9pg 提取码: ejmu （walk不需要改配置文件，其他两个需要下文有）
原文：bashLIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping

作者Tixiao Shan在2018年发表过LeGO-LOAM，当时他还在史蒂文斯理工学院读博士，19年毕业之后去了MIT做助理研究员（羡慕.jpg）。。。这篇文章LIO-SAM实际上是LeGO-LOAM的扩展版本，添加了IMU预积分因子和GPS因子，去除了帧帧匹配部分，然后更详细地描述了LeGO-LOAM帧图匹配部分的设计动机和细节。(引用于知乎大佬文章【论文阅读38】LIO-SAM)现在论文已经被IROS2020录用，作为高精度，imu，雷达，gps结合，程序还少的开源slam，非常值得学习。

需要安装的依赖：

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

安装gtsam4.0.2库：

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

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>

运行walk数据包不需要改params.yaml文件。其他两个数据包运行要修改topics和extrinsicRPY，extrinsicRot。需要保存pcd请修改保存true和路径。之后sudo gedit /opt/ros/kinetic/lib/python2.7/dist-packages/roslaunch/nodeprocess.py
调大_TIMEOUT_SIGINT值：
具体params.yaml配置修改：
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

lio_sam:# TopicspointCloudTopic: "points_raw"               # Point cloud dataimuTopic: "imu_correct"                         # IMU dataodomTopic: "odometry/imu"                   # IMU pre-preintegration odometry, same frequency as IMUgpsTopic: "odometry/gpsz"                   # GPS odometry topic from navsat, see module_navsat.launch file# GPS SettingsuseImuHeadingInitialization: false           # 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 dataposeCovThreshold: 25.0                      # m^2, threshold for using GPS data# Export settingssavePCD: true                              # https://github.com/TixiaoShan/LIO-SAM/issues/3savePCDDirectory: "/data/lio/"        # in your home folder, starts and ends with "/". Warning: the code deletes "LOAM" folder then recreates it. See "mapOptimization" for implementation# Sensor SettingsN_SCAN: 16                                  # 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 SettingsimuAccNoise: 3.9939570888238808e-03imuGyrNoise: 1.5636343949698187e-03imuAccBiasN: 6.4356659353532566e-05imuGyrBiasN: 3.5640318696367613e-05imuGravity: 9.80511# Extrinsics (lidar -> IMU)extrinsicTrans: [0.0, 0.0, 0.0]extrinsicRPY: [1,  0, 0,0, 1, 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 thresholdedgeThreshold: 1.0surfThreshold: 0.1edgeFeatureMinValidNum: 10surfFeatureMinValidNum: 100# voxel filter papramsodometrySurfLeafSize: 0.4                     # default: 0.4mappingCornerLeafSize: 0.2                    # default: 0.2mappingSurfLeafSize: 0.4                      # default: 0.4# robot motion constraint (in case you are using a 2D robot)z_tollerance: 1000                            # metersrotation_tollerance: 1000                     # radians# CPU ParamsnumberOfCores: 4                              # number of cores for mapping optimizationmappingProcessInterval: 0.15                  # seconds, regulate mapping frequency# Surrounding mapsurroundingkeyframeAddingDistThreshold: 1.0   # meters, regulate keyframe adding thresholdsurroundingkeyframeAddingAngleThreshold: 0.2  # radians, regulate keyframe adding thresholdsurroundingKeyframeDensity: 2.0               # meters, downsample surrounding keyframe poses   surroundingKeyframeSearchRadius: 50.0         # meters, within n meters scan-to-map optimization (when loop closure disabled)# Loop closureloopClosureEnableFlag: falsesurroundingKeyframeSize: 25                   # 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 closurehistoryKeyframeSearchTimeDiff: 30.0           # seconds, key frame that is n seconds older will be considered for loop closurehistoryKeyframeSearchNum: 25                  # number of hostory key frames will be fused into a submap for loop closurehistoryKeyframeFitnessScore: 0.3              # icp threshold, the smaller the better alignment# VisualizationglobalMapVisualizationSearchRadius: 1000.0    # meters, global map visualization radiusglobalMapVisualizationPoseDensity: 10.0       # meters, global map visualization keyframe densityglobalMapVisualizationLeafSize: 1.0           # meters, global map visualization cloud density# Navsat (convert GPS coordinates to Cartesian)
navsat:frequency: 50wait_for_datum: falsedelay: 0.0magnetic_declination_radians: 0yaw_offset: 0zero_altitude: truebroadcast_utm_transform: falsebroadcast_utm_transform_as_parent_frame: falsepublish_filtered_gps: false# EKF for Navsat
ekf_gps:publish_tf: falsemap_frame: mapodom_frame: odombase_link_frame: base_linkworld_frame: odomfrequency: 50two_d_mode: falsesensor_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_linkimu0_config: [false, false, false,true,  true,  true,false, false, false,false, false, true,true,  true,  true]imu0_differential: falseimu0_queue_size: 50 imu0_remove_gravitational_acceleration: true# -------------------------------------# Odometry (From Navsat):# -------------------------------------odom0: odometry/gpsodom0_config: [true,  true,  true,false, false, false,false, false, false,false, false, false,false, false, false]odom0_differential: falseodom0_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_ddotprocess_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]