ros运行包，Ubuntu20.04成功运行LIO-SAM

z@z:~/lio_sam_ws$ source devel/setup.bash 
z@z:~/lio_sam_ws$ roslaunch lio_sam run.launch

创建包链接：

链接1：Ubuntu20.04成功运行LIO-SAM_ubuntu20.04运行liosam-CSDN博客

链接2：ubuntu 20.04 ROS 编译和运行 lio-sam,并且导出PCD文件运行_ubuntu20.04 编译lio-sam-CSDN博客

z@z:~/lio_sam_ws$ source devel/setup.bash
z@z:~/lio_sam_ws$ roslaunch lio_sam run.launch

在bag包所在的位置打开新的终端运行程序代码：

rosbag play l1_cooperation_2023-12-21-15-44-16_velodyne.bag

rosbag play l1_cooperation_2023-12-21-15-44-16_velodyne.bag

最后得到三维点云图像

补充：

根据z@z:~/lio_sam_ws/src/LIO-SAM$ rosbag info l1_cooperation_2023-12-21-15-44-16_velodyne.bag
path:        l1_cooperation_2023-12-21-15-44-16_velodyne.bag
version:     2.0
duration:    10:12s (612s)
start:       Mar 28 2024 14:18:30.89 (1711606710.89)
end:         Mar 28 2024 14:28:43.88 (1711607323.88)
size:        15.4 GB
messages:    73561
compression: none [12163/12163 chunks]
types:       sensor_msgs/Imu         [6a62c6daae103f4ff57a132d6f95cec2]sensor_msgs/PointCloud2 [1158d486dd51d683ce2f1be655c3c181]
topics:      /back/velodyne_points    6130 msgs    : sensor_msgs/PointCloud2/imu/data               61304 msgs    : sensor_msgs/Imu        /velodyne_points         6127 msgs    : sensor_msgs/PointCloud2
怎么修改yaml文件里面的topic                                                                                                                                                           lio_sam:# TopicspointCloudTopic: "sensor_msgs/PointCloud2"               # Point cloud dataimuTopic: "sensor_msgs/Imu"                         # 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# FrameslidarFrame: "base_link"baselinkFrame: "base_link"odometryFrame: "odom"mapFrame: "map"# GPS SettingsuseImuHeadingInitialization: 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 dataposeCovThreshold: 25.0                      # m^2, threshold for using GPS data# Export settingssavePCD: false                              # https://github.com/TixiaoShan/LIO-SAM/issues/3savePCDDirectory: "/Downloads/LOAM/"        # in your home folder, starts and ends with "/". Warning: the code deletes "LOAM" folder then recreates it. See "mapOptimization" for implementation# Sensor Settingssensor: velodyne                            # lidar sensor type, 'velodyne' or 'ouster' or 'livox'N_SCAN: 16                                  # number of lidar channel (i.e., Velodyne/Ouster: 16, 32, 64, 128, Livox Horizon: 6)Horizon_SCAN: 1800                          # lidar horizontal resolution (Velodyne:1800, Ouster:512,1024,2048, Livox Horizon: 4000)downsampleRate: 1                           # default: 1. Downsample your data if too many points. i.e., 16 = 64 / 4, 16 = 16 / 1lidarMinRange: 1.0                          # default: 1.0, minimum lidar range to be usedlidarMaxRange: 1000.0                       # default: 1000.0, maximum lidar range to be used# IMU SettingsimuAccNoise: 3.9939570888238808e-03imuGyrNoise: 1.5636343949698187e-03imuAccBiasN: 6.4356659353532566e-05imuGyrBiasN: 3.5640318696367613e-05imuGravity: 9.80511imuRPYWeight: 0.01# Extrinsics: T_lb (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 thresholdedgeThreshold: 1.0surfThreshold: 0.1edgeFeatureMinValidNum: 10surfFeatureMinValidNum: 100# voxel filter papramsodometrySurfLeafSize: 0.4                     # default: 0.4 - outdoor, 0.2 - indoormappingCornerLeafSize: 0.2                    # default: 0.2 - outdoor, 0.1 - indoormappingSurfLeafSize: 0.4                     # default: 0.4 - outdoor, 0.2 - indoor# 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: trueloopClosureFrequency: 1.0                     # Hz, regulate loop closure constraint add frequencysurroundingKeyframeSize: 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 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 densityNavsat (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: falseEKF 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]

解答

根据您的ROS Bag文件中的Topic信息，需要将YAML配置文件中的 LiDAR点云Topic 和 IMU Topic 与实际录制的话题名称对齐。以下是具体修改步骤：---**一、关键Topic配置修改**
**1. LiDAR点云Topic**
原始配置错误地将点云Topic定义为消息类型，需改为实际Topic名称：
```yaml
lio_sam:# TopicspointCloudTopic: "/velodyne_points"  # 修改前："sensor_msgs/PointCloud2"# 或根据需求选择多雷达：# pointCloudTopic: "/back/velodyne_points"
```**2. IMU Topic**
IMU Topic需匹配Bag文件中的实际名称：
```yamlimuTopic: "/imu/data"  # 修改前："sensor_msgs/Imu"
```---**二、其他相关配置验证**
**1. 传感器类型与参数**
检查LiDAR型号参数是否匹配（Velodyne/Ouster/Livox）：
```yaml# Sensor Settingssensor: velodyne  # 确保与使用的LiDAR型号一致N_SCAN: 16        # Velodyne 16线雷达配置Horizon_SCAN: 1800 # Velodyne的水平分辨率
```**2. 坐标系对齐**
验证雷达与IMU的坐标系关系（若存在外参偏移需修改）：
```yaml# Extrinsics: T_lb (lidar -> imu)extrinsicTrans: [0.0, 0.0, 0.0]  # 若无偏移可保持默认extrinsicRot: [1, 0, 0,0, 1, 0,0, 0, 1]          # 单位矩阵表示坐标系对齐
```**3. 导航卫星配置**
如果使用GPS数据，确保`navsat`部分Topic与Bag一致：
```yaml
navsat:# 若有GPS数据需配置对应Topic# odom0: "odometry/gps"  # 取消注释并验证Topic名称
```---**三、修改后完整配置示例**
```yaml
lio_sam:# TopicspointCloudTopic: "/velodyne_points"  # 或 "/back/velodyne_points"imuTopic: "/imu/data"# FrameslidarFrame: "base_link"baselinkFrame: "base_link"odometryFrame: "odom"mapFrame: "map"# Sensor Settingssensor: velodyneN_SCAN: 16Horizon_SCAN: 1800downsampleRate: 1lidarMinRange: 1.0lidarMaxRange: 1000.0# IMU噪声参数（根据传感器标定结果填写）imuAccNoise: 3.9939570888238808e-03imuGyrNoise: 1.5636343949698187e-03imuGravity: 9.80511
```---**四、验证与调试**
1. 启动LIO-SAM：```bashroslaunch lio_sam run.launch```2. 回放Bag文件：```bashrosbag play l1_cooperation_2023-12-21-15-44-16_velodyne.bag \--topics /velodyne_points /imu/data  # 限定必要Topic```3. 检查Topic连通性：```bashrostopic list | grep -E "velodyne_points|imu/data"  # 确认Topic已发布```---**五、常见问题解决**
1. Topic未接收：• 使用`rostopic hz /velodyne_points`检查发布频率• 在`launch`文件中添加重映射：```xml<node pkg="lio_sam" type="lio_sam_imuPreintegration" name="imuPreintegration"><remap from="/imu/data" to="/your_custom_imu_topic"/></node>```2. 时间同步问题：```bashrosbag play --clock bag_file.bag  # 启用模拟时间```---通过以上配置，您的LIO-SAM将正确订阅Bag文件中的传感器数据。若需进一步优化建图效果，可调整`mappingCornerLeafSize`等点云降采样参数（参见原始配置第25-27行）。

问题2：

为什么修改之后运行重新打开一个终端，输入roslaunch lio_sam run.launch和rosbag play 包在rviz里面没有显示呀，是需要添加什么话题吗

---**原因分析与解决方案**
根据图片中的 RViz报错信息 和 终端日志，修改后运行未显示数据的原因可能是以下几点：---**1. 坐标系（TF）未正确发布**
图片中的关键错误是 `Fixed Frame [map] does not exist`，说明ROS系统中未发布 `map` 坐标系，导致所有依赖该坐标系的显示内容（点云、轨迹等）无法渲染。  
原因：  
• LIO-SAM的核心节点未正常启动（如 `mapOptimization` 节点崩溃）。  • YAML参数配置错误（如 `mapFrame` 与其他节点冲突）。  解决方案：  
1. 检查YAML配置：确保 `lio_sam` 部分的 `mapFrame` 和 `odometryFrame` 与其他节点（如 `ekf_gps`）的配置一致：  ```yaml# lio_sam/config/params.yamllio_sam:mapFrame: "map"          # 必须与其他节点的map_frame一致odometryFrame: "odom"    # 必须与ekf_gps的odom_frame一致``````yaml# ekf_gps配置（若有）ekf_gps:map_frame: "map"odom_frame: "odom"base_link_frame: "base_link"```2. 验证节点启动状态：  • 在终端日志中，确认 `lio_sam_mapOptmization` 节点是否启动（如日志中出现 `[lio_sam_mapOptmization-4] process started`）。  • 若节点崩溃，检查 `mapOptmization.cpp` 的依赖项（如GTSAM版本、PCL配置）。---**2. 话题未正确订阅**
RViz中未显示点云或轨迹，可能是因为 话题名称未匹配 或 数据未发布。  
原因：  
• YAML中的 `pointCloudTopic` 或 `imuTopic` 与Bag文件中的实际话题名称不一致。  • 数据未同步（如Bag文件播放时未指定时钟）。  解决方案：  
1. 修改YAML话题名称：  根据Bag文件中的实际话题名称调整参数：  ```yaml# lio_sam/config/params.yamllio_sam:pointCloudTopic: "/velodyne_points"  # 修改为Bag中的实际名称imuTopic: "/imu/data"               # 修改为Bag中的实际名称```2. 指定时钟同步播放Bag：  ```bashrosbag play --clock your_data.bag  # 启用模拟时钟```---**3. RViz显示配置错误**
即使数据已发布，若RViz未正确配置显示类型或话题，仍无法可视化。  
解决方案：  
1. 添加显示插件：  • 在RViz中点击 Add → By Topic → 选择以下话题：  ◦ PointCloud2：订阅 `/lio_sam/mapping/cloud_registered`  ◦ Path：订阅 `/lio_sam/mapping/path`  ◦ TF：勾选所有坐标系  • 设置 Fixed Frame 为 `map`（若不存在则设为 `odom` 临时测试）。2. 验证话题存在性：  ```bashrostopic list | grep -E "cloud_registered|path|imu|odom"  # 确认关键话题已发布```---**4. 进程依赖冲突**
终端日志显示 `robot_state_publisher` 和 `ekf_gps` 进程异常退出，可能是 节点依赖冲突 或 参数重复定义。  
解决方案：  
1. 关闭冲突节点：  在 `run.launch` 中注释或删除重复节点定义（如 `robot_state_publisher` 或 `ekf_gps`）。  ```xml<!-- 注释冲突节点 --><!-- <node pkg="robot_state_publisher" type="robot_state_publisher" name="robot_state_publisher" /> -->```2. 检查launch文件依赖：  确保所有节点依赖的包已安装（如 `robot_state_publisher` 需安装 `ros-noetic-robot-state-publisher`）。---**操作流程验证**
1. 启动LIO-SAM：  ```bashsource devel/setup.bash  # 确保环境生效roslaunch lio_sam run.launch```2. 播放数据包：  ```bashrosbag play --clock your_data.bag --topics /velodyne_points /imu/data  # 限定必要话题```3. RViz配置检查：  • 打开RViz并加载LIO-SAM的默认配置（通常位于 `config/rviz.rviz`）。  • 手动添加显示插件并订阅话题（见上文步骤）。---**常见问题排查**
• 终端无地图输出：检查 `mapOptmization` 节点的日志，确认GTSAM优化是否收敛。  • TF树不完整：运行 `rosrun tf view_frames` 生成TF关系图，验证 `map → odom → base_link` 是否存在。  • 数据延迟：调整Bag播放速率 `rosbag play -r 0.8`（降速80%）。通过以上步骤，可解决坐标系缺失和数据显示问题。若仍无法解决，请提供完整终端日志和 `params.yaml` 文件进一步分析。

最后得到的三维点云图像如下：