正文

ros实验笔记之——slam无人驾驶初入门(代码片段)

gwpscut  gwpscut  2023-01-06  662

关键词：

最近想学习一下无人驾驶SLAM方面的内容

代码测试

这里先基于kitti数据集，进行测试。之前博客中已经介绍过kitti数据集了。本博文就用这个数据集来进行各种经典方法的复现

The KITTI Vision Benchmark Suitehttp://www.cvlibs.net/datasets/kitti/eval_odometry.php

VINS-FUSION

VINS-Fusion demo

把vins-mono也配置一下好了~

GitHub - HKUST-Aerial-Robotics/VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimatorhttps://github.com/HKUST-Aerial-Robotics/VINS-Mono

主要cm的时候会报错，把里面cmakelist的c++11改为14即可

这样可以避免出错，但好像运行会报错

试试改为ceres-solver-1.14.0。再编译。发现还是不可以。。。。重装系统看看。

在另外一台电脑上尝试了不行，然后把vins-fusion也编译了一下,然后运行过vins-fusion后就可以了？？？再运行好就work了。。。奇怪。。。。

最终发现问题了，要按下面顺序运行才可以。。。cao

这个原因非常的迷惑。。。。。

roslaunch vins_estimator vins_rviz.launch
roslaunch vins_estimator euroc.launch

下载数据集kmavvisualinertialdatasets – ASL Datasetshttps://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets

vins系列真的非常丰富，下面还有个co-vins

https://github.com/qintonguav/Co-VINShttps://github.com/qintonguav/Co-VINS

A-LOAM

之前博客《学习笔记之——激光雷达SLAM（LOAM系列的复现与学习）》已经配置过A-LOAM了。本博文来跑一下其kitti数据集

GitHub - HKUST-Aerial-Robotics/A-LOAM: Advanced implementation of LOAMAdvanced implementation of LOAM. Contribute to HKUST-Aerial-Robotics/A-LOAM development by creating an account on GitHub.https://github.com/HKUST-Aerial-Robotics/A-LOAM

注意，对于下载的数据集需要按以下的形式打包放好

data
 |----poses
 |     |----00.txt
 |----sequences
 |     |----00
 |     |   |----image_0
 |     |   |----image_1
 |     |   |----velodyne
 |     |   |----time.txt

然后将kitti的launch修改为：

<launch>
    

    <node name="kittiHelper" pkg="aloam_velodyne" type="kittiHelper" output="screen"> 
        <!-- <param name="dataset_folder" type="string" value="/data/KITTI/odometry/" /> -->
        <param name="dataset_folder" type="string" value="/home/kwanwaipang/dataset/kitti/data/" />
        <param name="sequence_number" type="string" value="00" />
        <!-- <param name="to_bag" type="bool" value="false" /> -->
        <param name="to_bag" type="bool" value="true" />
        <!-- <param name="output_bag_file" type="string" value="/tmp/kitti.bag" /> replace with your output folder -->
        <param name="output_bag_file" type="string" value="/home/kwanwaipang/dataset/kitti/kitti_00.bag" /> <!-- replace with your output folder -->
        <param name="publish_delay" type="int" value="1" />
    </node>
</launch>

好坑。。上面那样的list是不对的。要改为下面这样才对（建议直接看源代码）

运行效果如下图所示

视频连接：

aloam

LIO-SAM

https://github.com/TixiaoShan/LIO-SAMhttps://github.com/TixiaoShan/LIO-SAM

有可能出现缺乏依赖libmetis.so而报错。运行下面命令安装即可

sudo apt-get install -y libmetis-dev

如若需要允许kitti数据集，则需要将config里面的参数文件修改如下:

lio_sam:
 
  # Topics
  pointCloudTopic: "points_raw"               # Point cloud data
  # imuTopic: "imu_raw"                         # IMU data
  imuTopic: "imu_correct"                         # 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
 
  # 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: false                              # https://github.com/TixiaoShan/LIO-SAM/issues/3
  savePCDDirectory: "/Downloads/LOAM/"        # 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: 64   #16                                  # number of lidar channel (i.e., 16, 32, 64, 128)
  Horizon_SCAN: 1800                          # lidar horizontal resolution (Velodyne:1800, Ouster:512,1024,2048)
  downsampleRate: 2  #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: [-8.086759e-01, 3.195559e-01, -7.997231e-01]
  extrinsicRot:  [9.999976e-01, 7.553071e-04, -2.035826e-03, -7.854027e-04, 9.998898e-01, -1.482298e-02, 2.024406e-03, 1.482454e-02, 9.998881e-01]
  extrinsicRPY: [9.999976e-01, 7.553071e-04, -2.035826e-03, -7.854027e-04, 9.998898e-01, -1.482298e-02, 2.024406e-03, 1.482454e-02, 9.998881e-01]
  # 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]

同样的类似ALOAM，作者也给出了从kitti生成rosbag的代码

To generate more bags using other KITTI raw data, you can use the python script provided in "config/doc/kitti2bag".

视频效果如下：

LIO-SAM

LIO-Mapping:A Tightly Coupled 3D Lidar and Inertial Odometry and Mapping Approach

GitHub - hyye/lio-mapping: Implementation of Tightly Coupled 3D Lidar Inertial Odometry and Mapping (LIO-mapping)https://github.com/hyye/lio-mapping

Stereo Visual Inertial Pose Estimation Based on Feedforward-Feedback Loops

这是港理工的一个开源项目。论文链接见：https://arxiv.org/pdf/2007.02250.pdf

按照要求配置看看~

GitHub - HKPolyU-UAV/FLVIS: FLVIS: Feedback Loop Based Visual Initial SLAMhttps://github.com/HKPolyU-UAV/FLVIS

如果kitti报错

(.text.startup+0x4e8)：对‘Sophus::SE3::SE3()’未定义的引用

可以参考（Sophus 编译错误_u010003609的博客-CSDN博客）

将对应的cmkaelist文件改为

cmake_minimum_required(VERSION 2.8.3)
project(flvis)

add_definitions(-std=c++11)
#set(CMAKE_CXX_FLAGS "-std=c++11)
set(CMAKE_CXX_FLAGS "-std=c++11 $CMAKE_CXX_FLAGS -O3 -Wall -pthread") # -Wextra -Werror
set(CMAKE_BUILD_TYPE "RELEASE")


list(APPEND CMAKE_MODULE_PATH $CMAKE_CURRENT_SOURCE_DIR/3rdPartLib/g2o/cmake_modules)
set(G2O_ROOT /usr/local/include/g2o)
find_package(G2O REQUIRED) 

find_package (OpenCV 3 REQUIRED)
find_package (Eigen3 REQUIRED)

find_package (CSparse REQUIRED )
find_package (Sophus REQUIRED )
find_package (yaml-cpp REQUIRED )
find_package (DBoW3 REQUIRED)
# pcl
find_package( PCL REQUIRED)
include_directories( $PCL_INCLUDE_DIRS )
add_definitions( $PCL_DEFINITIONS )

#FIND_PACKAGE(octomap REQUIRED )
#FIND_PACKAGE(octovis REQUIRED )
#INCLUDE_DIRECTORIES($OCTOMAP_INCLUDE_DIRS)

find_package(catkin REQUIRED COMPONENTS
    nodelet
    roscpp
    rostime
    std_msgs
    sensor_msgs
    geometry_msgs
    nav_msgs
    pcl_ros
    tf
    visualization_msgs
    image_transport
    cv_bridge
    message_generation
    message_filters
    )

add_message_files(
    FILES
    KeyFrame.msg
    CorrectionInf.msg
    )

generate_messages(
    DEPENDENCIES
    std_msgs
    sensor_msgs
    geometry_msgs
    nav_msgs
    visualization_msgs
    )

## Declare a catkin package
catkin_package(
    CATKIN_DEPENDS message_runtime
    )


include_directories(
    $catkin_INCLUDE_DIRS
    $OpenCV_INCLUDE_DIRS
    $G2O_INCLUDE_DIRS
    $CSPARSE_INCLUDE_DIR
    $Sophus_INCLUDE_DIRS
    $YAML_CPP_INCLUDE_DIR
    $DBoW3_INCLUDE_DIR
    "$CMAKE_CURRENT_SOURCE_DIR/src/"
    "$CMAKE_CURRENT_SOURCE_DIR/src/processing/"
    "$CMAKE_CURRENT_SOURCE_DIR/src/backend/"
    "$CMAKE_CURRENT_SOURCE_DIR/src/frontend/"
    "$CMAKE_CURRENT_SOURCE_DIR/src/utils/"
    "$CMAKE_CURRENT_SOURCE_DIR/src/visualization/"
    #"$CMAKE_CURRENT_SOURCE_DIR/src/octofeeder/"
    )


SET(G2O_LIBS cholmod cxsparse -lg2o_cli -lg2o_core
    -lg2o_csparse_extension -lg2o_ext_freeglut_minimal -lg2o_incremental
    -lg2o_interactive -lg2o_interface -lg2o_opengl_helper -lg2o_parser
    -lg2o_simulator -lg2o_solver_cholmod -lg2o_solver_csparse
    -lg2o_solver_dense -lg2o_solver_pcg -lg2o_solver_slam2d_linear
    -lg2o_solver_structure_only -lg2o_stuff -lg2o_types_data -lg2o_types_icp
    -lg2o_types_sba -lg2o_types_sclam2d -lg2o_types_sim3 -lg2o_types_slam2d
    -lg2o_types_slam3d)

## Declare a C++ library
add_library(flvis
    #processing
    src/processing/feature_dem.cpp
    src/processing/depth_camera.cpp
    src/processing/landmark.cpp
    src/processing/camera_frame.cpp
    src/processing/triangulation.cpp
    src/processing/lkorb_tracking.cpp
    src/processing/imu_state.cpp
    src/processing/vi_motion.cpp
    src/processing/optimize_in_frame.cpp
    #vis
    src/visualization/rviz_frame.cpp
    src/visualization/rviz_path.cpp
    src/visualization/rviz_pose.cpp
    src/visualization/rviz_odom.cpp
    #msg
    src/utils/keyframe_msg.cpp
    src/utils/correction_inf_msg.cpp

    #node tracking
    src/frontend/vo_tracking.cpp
    src/frontend/f2f_tracking.cpp
    #node localmap
    src/backend/vo_localmap.cpp
    #node loop closing
    src/backend/vo_loopclosing.cpp
    src/backend/poselmbag.cpp

    #src/octofeeder/octomap_feeder.cpp
    )

add_dependencies(flvis
    flvis_generate_messages_cpp
    $catkin_EXPORTED_TARGETS)

target_link_libraries(flvis
    $catkin_LIBRARIES
    $OpenCV_LIBRARIES
    $CSPARSE_LIBRARY
    $Sophus_LIBRARIES
    $YAML_CPP_LIBRARIES
    $DBoW3_LIBRARIES
    $G2O_LIBS
    $PCL_LIBRARIES
    $Boost_SYSTEM_LIBRARY
    #$OCTOMAP_LIBRARIES
    )

#independent modules
#1 euroc_publisher publish path
add_executable(vo_repub_rec
    src/independ_modules/vo_repub_rec.cpp)
target_link_libraries(vo_repub_rec
    $catkin_LIBRARIES
    $Sophus_LIBRARIES)

add_executable(kitti_publisher
    src/independ_modules/kitti_publisher.cpp
    src/visualization/rviz_path.cpp)
set(Sophus_LIBRARIES libSophus.so)
target_link_libraries(kitti_publisher
    $catkin_LIBRARIES
    $Sophus_LIBRARIES)

注意，可能出现g2o安装不好导致有问题，重新装一下即可

修改对应的launch文件，数据集还是上面整理好的

<?xml version="1.0"?>
<launch>

<!--Input######################################################################################################-->
    <param name="/dataset_pub_delay"      type="double"  value="5.0" />
    <param name="/dataset_pub_rate"       type="int"     value="30" />

    <param name="/publish_gt"             type="bool"    value="true" />
    <param name="/dataset_folder_path"    type="string"  value="/home/kwanwaipang/dataset/kitti/data/sequences/00/" />
    <param name="/dataset_gt_file"        type="string"  value="/home/kwanwaipang/dataset/kitti/data/results/00.txt" />
    <node pkg="flvis" type="kitti_publisher" name="kitti_publisher" output="screen"/>

<!--FLVIS######################################################################################################-->
    <arg name="node_start_delay"  default="1.0" />
    <param name="/yamlconfigfile" type="string" value="$(find flvis)/launch/KITTI/KITTI.yaml"/>
    <param name="/voc"            type="string" value="$(find flvis)/voc/voc_orb.dbow3"/>

    <!-- Manager -->
    <node pkg="nodelet" type="nodelet"
          name="flvis_nodelet_manager" args="manager" output="screen"
          launch-prefix="bash -c 'sleep $(arg node_start_delay); $0 $@' ">
          <param name="num_worker_threads" value="4" />
    </node>

    <!-- TrackingNode -->
    <!-- D435i -->
    <node pkg="nodelet" type="nodelet" args="load flvis/TrackingNodeletClass flvis_nodelet_manager"
        name="TrackingNodeletClass_loader" output="screen"
        launch-prefix="bash -c 'sleep $(arg node_start_delay); $0 $@' ">
        <remap from="/imu"               to="/camera/imu"/>
    </node>

    <!-- LocalMapNode -->
    <!--window_size: Num of keyframes in sliding window optimizer-->
<!--    <node pkg="nodelet" type="nodelet" args="load flvis/LocalMapNodeletClass flvis_nodelet_manager"
          name="LocalMapNodeletClass_loader" output="screen"
          launch-prefix="bash -c 'sleep $(arg node_start_delay); $0 $@' ">
        <param name="/window_size" type="int" value="8" />
    </node>-->

    <!-- LoopClosingNode -->
    <node pkg="nodelet" type="nodelet" args="load flvis/LoopClosingNodeletClass flvis_nodelet_manager"
        name="LoopClosingNodeletClass_loader" output="screen"
        launch-prefix="bash -c 'sleep $(arg node_start_delay); $0 $@' ">
    </node>

<node pkg="flvis" type="vo_repub_rec" name="lc2file" output="screen">
    <!--Sub Support Type:-->
    <param name="sub_type" type="string" value="NavPath" />
    <param name="sub_topic" type="string" value="/vision_path_lc_all" />

    <!--Support Type:-->
    <!--"0" disable the republish function -->
    <!--"Path"-->
    <!--"PoseStamped"-->
    <param name="repub_type" type="string" value="0" />
    <param name="repub_topic" type="string" value="/republish_path" />
    <!--output_file_path = "0" disable the file output function-->
    <param name="output_file_path" type="string" value="$(find flvis)/results/kitti_lc.txt" />
</node>


</launch>

roslaunch flvis rviz_kitti.launch
roslaunch flvis flvis_kitti.launch

效果如下

flvis

AVP-SLAM: Semantic Visual Mapping and Localization for Autonomous Vehicles in the Parking Lot

GitHub - qintonguav/AVP-SLAM-SIM: A basic implementation(not official code) of AVP-SLAM(IROS 2020) in simulation. https://arxiv.org/abs/2007.01813https://github.com/qintonguav/AVP-SLAM-SIM

ORB-SLAM 跑kitti

GVINS: Tightly Coupled GNSS-Visual-Inertial Fusion for Smooth and Consistent State Estimation

GitHub - HKUST-Aerial-Robotics/GVINS: Tightly coupled GNSS-Visual-Inertial system for locally smooth and globally consistent state estimation in complex environment.https://github.com/HKUST-Aerial-Robotics/GVINS

SuMa++: Efficient LiDAR-based Semantic SLAM

https://github.com/PRBonn/semantic_sumahttps://github.com/PRBonn/semantic_suma

DSO: Direct Sparse Odometry

DSO跑KITTI数据集_SLAM的博客-CSDN博客安装DSO此步非常简单，安装也很快，不详述，【参考博客】或【GitHub源网页】写在前面数据集中的camera.txt文件是可以更改的，且需要改成对应的相机参数图片的大小可以不用更改为一般的1280x1024，就保持KITTI数据集的图片尺寸就行图片的扩展名不用更改times.txt文件也可以不自己做（取巧的办法）可以不用压缩包直接解压选择图片文件夹就行需要注意的问题首先可...https://blog.csdn.net/weixin_43166819/article/details/103133570

DSO安装与调试 - huicanlin - 博客园

OverlapNet - Loop Closing for 3D LiDAR-based SLAM

https://github.com/PRBonn/OverlapNet

High-speed Autonomous Drifting with Deep Reinforcement Learning

GitHub - caipeide/drift_drl: High-speed Autonomous Drifting with Deep Reinforcement Learning

https://sites.google.com/view/autonomous-drifting-with-drl

参考资料

ros实验笔记之——基于l515激光相机的flvis与mlmapping(代码片段)

之前博客《ROS实验笔记之——VINS-Mono在l515上的实现》在l515上实现了vins，博客《ROS实验笔记之——SLAM无人驾驶初入门》配置flvis并跑了对应的kitti数据集本博文在l515上先实现flvis然后再用mlmapping来建图。camera.launch<launch>&l... 查看详情

ros实验笔记之——vins-mono在l515上的实现(代码片段)

之前博客《ROS实验笔记之——IntelRealsensel515激光相机的使用》实现了用l515运行RTABmap，本博文试试在l515上实现vins-mono首先需要将vins-mono配置成功，如果出现像之前博客《ROS实验笔记之——SLAM无人驾驶初入门》提到的运行... 查看详情

ros实验笔记之——px4仿真

...些基本的仿真学习～之前在做无人机仿真开发时《ROS实验笔记之——基于Prometheus自主无人机开源项目的学习与仿真》，已经安装了px4了～为此不需要查看详情

ros实验笔记之——无人机在vicion下试飞(代码片段)

之前博《ROS实验笔记之——自主搭建四旋翼无人机》、《ROS实验笔记之——JCV-450无人机初入门》、《ROS实验笔记之——基于Prometheus自主无人机开源项目的学习与仿真》已经介绍过一些无人机的仿真，试飞。本博文基于vicion... 查看详情

ros实验笔记之——基于cartographer的多机器人slam地图融合(代码片段)

...图融合》已经实现了基于gmapping的多机器人地图融合。实验和仿真都验证过了。本博文通过cartographer来实现SLAM，再做mapmerge先看视频效果two启动的文件#!/bin/bashgnome-terminal--tab-e'bash-c"roscore;execbash"'sl 查看详情

ros实验笔记之——基于cartographer方法的slam(代码片段)

之前做的2DLiDAR的实验中，大多采用gmapping，本博文试试采用cartographer。对于cartographer方法，相比起gmapping，其多了IMU以及回环检测。有了回环检测后，可以避免odom的累积误差首先安装依赖包sudoapt-getinstallros-melo... 查看详情

ros实验笔记之——基于prometheus的无人机运动规划(代码片段)

...学习无人机的运动规划。关于该项目的配置可以参考《ROS实验笔记之——基于Prometheus自主无人机开源项目的学习与仿真》Demo演示基于2D-LiDAR的APF路径规划基于rgbdcamera的APF路径规划基于3D-LiDAR的Astar路径规划ego-planner对于ego-planner&#... 查看详情

ros学习笔记之——px4生态系统

...步的学习PX4的相关知识，部分资料来源于购买的阿木实验室的课程《PX4智能无人机二次开发》与《Prometheus自主无人机二次开发课程》目录 PX4的生态Dronecode基金会PX4飞控硬件-PixhawkQGCMAVLink通信协议 Mavros功能包无人机中三... 查看详情

ros学习记录17slam仿真学习6完结——无人驾驶(代码片段)

零.前言这次使用Move_Base框架配置配置，就能实现自主导航了。仿真篇结束，后面找机会拿个实物来玩儿。在整理包，后面会传到github和gitee上一.安装与介绍1.1move_base安装sudoapt-getinstallros-noetic-move-base框架是这样，... 查看详情

ros实验笔记之——p450无人机

参考资料P450使用手册——教程演示_哔哩哔哩_bilibili使用文档|阿木实验室查看详情

ros学习笔记之——3dmapmerge(代码片段)

之前博客《ROS实验笔记之——基于cartographer的多机器人SLAM地图融合》已经实现了2Dmapmerge，也做了大量这方面的demotwo基于两个turtlebot2的多机器人SLAM地图融合本博文看看3dmapmerge方面的资料首先呢有CCM-SLAM（https://github.com/VI... 查看详情

ros实验笔记之——uzh-fpv数据集的验证

UZH-FPV是一个在快速飞行的无人机上采集事件、图像等数据等数据集。该数据集对于基于事件及图像的VIO、VO都具备较大的挑战。要做出有效、鲁棒的视觉里程计算法的同行们，都建议去evaluat一下哈。TheUZHFPVDataset这里给出我... 查看详情

无人驾驶系列基于ros的无人驾驶系统

...化。创立PerceptIn之前在百度美国研发中心工作，负责无人车系统架构及产品化。张伟德，百度美国研发中心高级架构师。曾在弗吉尼亚大学网格计算小组担任研究员，在Yahoo!、微软等公司负责查看详情

ros实验笔记之——ceres跟eigen不匹配(代码片段)

slam中经常需要用到基于ceres的优化，但是有时eigen库更新了或者对应的gcc版本影响编译之类的，会出现eigen的版本根ceres版本不匹配。实际上只需要进入对应的文件更改依赖即可。当然提前需要自己的eigen版本号。可参考查... 查看详情

ros实验笔记之——uzh-fpv数据集的验证

先给出效果如下ComparisonwithVINS-Mono,VINS-Fusion,andORB-SLAM3inFPV-Dataset 查看详情

ros实验笔记之——evo(代码片段)

Errorwhileprocessing"dvs_trackingflyingroom"·Issue#4·uzh-rpg/rpg_dvs_evo_open·GitHub之前博客《ROS仿真笔记之——基于gazebo的eventcamera仿真(dvsgazebo)》介绍了DVS的gazebo仿真。博客《ROS学习笔记之——ESVO复现及DAVIS346测试》介绍了ESVO以及事件... 查看详情

ros实验笔记之——davis346测试(代码片段)

之前博客《ROS学习笔记之——ESVO复现及DAVIS346测试》介绍了dvs驱动的安装。本博文对到手的时间相机进行测试。运行命令roslaunchdvs_rendererdavis_mono.launch但是运行的时候出现报错[WARN][1632472696.858249337]:Cameracalibrationfile/home/kwanwaipang/.r... 查看详情

ros实验笔记之——intelrealsensel515激光相机的使用(代码片段)

最近实验室购买了IntelRealsensel515相机。本博文记录使用过程~ 驱动安装先到官网安装驱动：https://github.com/IntelRealSense/realsense-roshttps://github.com/IntelRealSense/realsense-ros安装realsense2_camerasudoapt-getinstallros-melod 查看详情