关键词：
之前博客《ROS实验笔记之——Intel Realsense l515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》提到的运行出错的问题也只能按里面的方法改改了。。。。
首先可以先运行
roslaunch realsense2_camera rs_camera.launch 看对应的launch文件
<launch>
<arg name="serial_no" default=""/>
<arg name="usb_port_id" default=""/>
<arg name="device_type" default=""/>
<arg name="json_file_path" default=""/>
<arg name="camera" default="camera"/>
<arg name="tf_prefix" default="$(arg camera)"/>
<arg name="external_manager" default="false"/>
<arg name="manager" default="realsense2_camera_manager"/>
<arg name="output" default="screen"/>
<arg name="respawn" default="false"/>
<arg name="fisheye_width" default="-1"/>
<arg name="fisheye_height" default="-1"/>
<arg name="enable_fisheye" default="false"/>
<arg name="depth_width" default="-1"/>
<arg name="depth_height" default="-1"/>
<arg name="enable_depth" default="true"/>
<arg name="confidence_width" default="-1"/>
<arg name="confidence_height" default="-1"/>
<arg name="enable_confidence" default="true"/>
<arg name="confidence_fps" default="-1"/>
<arg name="infra_width" default="848"/>
<arg name="infra_height" default="480"/>
<arg name="enable_infra" default="false"/>
<arg name="enable_infra1" default="false"/>
<arg name="enable_infra2" default="false"/>
<arg name="infra_rgb" default="false"/>
<arg name="color_width" default="-1"/>
<arg name="color_height" default="-1"/>
<arg name="enable_color" default="true"/>
<arg name="fisheye_fps" default="-1"/>
<arg name="depth_fps" default="-1"/>
<arg name="infra_fps" default="30"/>
<arg name="color_fps" default="-1"/>
<arg name="gyro_fps" default="-1"/>
<arg name="accel_fps" default="-1"/>
<arg name="enable_gyro" default="false"/>
<arg name="enable_accel" default="false"/>
<arg name="enable_pointcloud" default="false"/>
<arg name="pointcloud_texture_stream" default="RS2_STREAM_COLOR"/>
<arg name="pointcloud_texture_index" default="0"/>
<arg name="allow_no_texture_points" default="false"/>
<arg name="ordered_pc" default="false"/>
<arg name="enable_sync" default="false"/>
<arg name="align_depth" default="false"/>
<arg name="publish_tf" default="true"/>
<arg name="tf_publish_rate" default="0"/>
<arg name="filters" default=""/>
<arg name="clip_distance" default="-2"/>
<arg name="linear_accel_cov" default="0.01"/>
<arg name="initial_reset" default="false"/>
<arg name="unite_imu_method" default=""/>
<arg name="topic_odom_in" default="odom_in"/>
<arg name="calib_odom_file" default=""/>
<arg name="publish_odom_tf" default="true"/>
<arg name="stereo_module/exposure/1" default="7500"/>
<arg name="stereo_module/gain/1" default="16"/>
<arg name="stereo_module/exposure/2" default="1"/>
<arg name="stereo_module/gain/2" default="16"/>
<group ns="$(arg camera)">
<include file="$(find realsense2_camera)/launch/includes/nodelet.launch.xml">
<arg name="tf_prefix" value="$(arg tf_prefix)"/>
<arg name="external_manager" value="$(arg external_manager)"/>
<arg name="manager" value="$(arg manager)"/>
<arg name="output" value="$(arg output)"/>
<arg name="respawn" value="$(arg respawn)"/>
<arg name="serial_no" value="$(arg serial_no)"/>
<arg name="usb_port_id" value="$(arg usb_port_id)"/>
<arg name="device_type" value="$(arg device_type)"/>
<arg name="json_file_path" value="$(arg json_file_path)"/>
<arg name="enable_pointcloud" value="$(arg enable_pointcloud)"/>
<arg name="pointcloud_texture_stream" value="$(arg pointcloud_texture_stream)"/>
<arg name="pointcloud_texture_index" value="$(arg pointcloud_texture_index)"/>
<arg name="enable_sync" value="$(arg enable_sync)"/>
<arg name="align_depth" value="$(arg align_depth)"/>
<arg name="fisheye_width" value="$(arg fisheye_width)"/>
<arg name="fisheye_height" value="$(arg fisheye_height)"/>
<arg name="enable_fisheye" value="$(arg enable_fisheye)"/>
<arg name="depth_width" value="$(arg depth_width)"/>
<arg name="depth_height" value="$(arg depth_height)"/>
<arg name="enable_depth" value="$(arg enable_depth)"/>
<arg name="confidence_width" value="$(arg confidence_width)"/>
<arg name="confidence_height" value="$(arg confidence_height)"/>
<arg name="enable_confidence" value="$(arg enable_confidence)"/>
<arg name="confidence_fps" value="$(arg confidence_fps)"/>
<arg name="color_width" value="$(arg color_width)"/>
<arg name="color_height" value="$(arg color_height)"/>
<arg name="enable_color" value="$(arg enable_color)"/>
<arg name="infra_width" value="$(arg infra_width)"/>
<arg name="infra_height" value="$(arg infra_height)"/>
<arg name="enable_infra" value="$(arg enable_infra)"/>
<arg name="enable_infra1" value="$(arg enable_infra1)"/>
<arg name="enable_infra2" value="$(arg enable_infra2)"/>
<arg name="infra_rgb" value="$(arg infra_rgb)"/>
<arg name="fisheye_fps" value="$(arg fisheye_fps)"/>
<arg name="depth_fps" value="$(arg depth_fps)"/>
<arg name="infra_fps" value="$(arg infra_fps)"/>
<arg name="color_fps" value="$(arg color_fps)"/>
<arg name="gyro_fps" value="$(arg gyro_fps)"/>
<arg name="accel_fps" value="$(arg accel_fps)"/>
<arg name="enable_gyro" value="$(arg enable_gyro)"/>
<arg name="enable_accel" value="$(arg enable_accel)"/>
<arg name="publish_tf" value="$(arg publish_tf)"/>
<arg name="tf_publish_rate" value="$(arg tf_publish_rate)"/>
<arg name="filters" value="$(arg filters)"/>
<arg name="clip_distance" value="$(arg clip_distance)"/>
<arg name="linear_accel_cov" value="$(arg linear_accel_cov)"/>
<arg name="initial_reset" value="$(arg initial_reset)"/>
<arg name="unite_imu_method" value="$(arg unite_imu_method)"/>
<arg name="topic_odom_in" value="$(arg topic_odom_in)"/>
<arg name="calib_odom_file" value="$(arg calib_odom_file)"/>
<arg name="publish_odom_tf" value="$(arg publish_odom_tf)"/>
<arg name="stereo_module/exposure/1" value="$(arg stereo_module/exposure/1)"/>
<arg name="stereo_module/gain/1" value="$(arg stereo_module/gain/1)"/>
<arg name="stereo_module/exposure/2" value="$(arg stereo_module/exposure/2)"/>
<arg name="stereo_module/gain/2" value="$(arg stereo_module/gain/2)"/>
<arg name="allow_no_texture_points" value="$(arg allow_no_texture_points)"/>
<arg name="ordered_pc" value="$(arg ordered_pc)"/>
</include>
</group>
</launch>查看一下topic
rostopic list
/camera/color/camera_info
/camera/color/image_raw
/camera/color/image_raw/compressed
/camera/color/image_raw/compressed/parameter_descriptions
/camera/color/image_raw/compressed/parameter_updates
/camera/color/image_raw/compressedDepth
/camera/color/image_raw/compressedDepth/parameter_descriptions
/camera/color/image_raw/compressedDepth/parameter_updates
/camera/color/image_raw/theora
/camera/color/image_raw/theora/parameter_descriptions
/camera/color/image_raw/theora/parameter_updates
/camera/depth/camera_info
/camera/depth/image_rect_raw
/camera/depth/image_rect_raw/compressed
/camera/depth/image_rect_raw/compressed/parameter_descriptions
/camera/depth/image_rect_raw/compressed/parameter_updates
/camera/depth/image_rect_raw/compressedDepth
/camera/depth/image_rect_raw/compressedDepth/parameter_descriptions
/camera/depth/image_rect_raw/compressedDepth/parameter_updates
/camera/depth/image_rect_raw/theora
/camera/depth/image_rect_raw/theora/parameter_descriptions
/camera/depth/image_rect_raw/theora/parameter_updates
/camera/extrinsics/depth_to_color
/camera/l500_depth_sensor/parameter_descriptions
/camera/l500_depth_sensor/parameter_updates
/camera/motion_module/parameter_descriptions
/camera/motion_module/parameter_updates
/camera/realsense2_camera_manager/bond
/camera/rgb_camera/parameter_descriptions
/camera/rgb_camera/parameter_updates
/diagnostics
/rosout
/rosout_agg
/tf
/tf_static
没有发现有IMU，该一下命令为：
roslaunch realsense2_camera rs_camera.launch \\
align_depth:=true \\
unite_imu_method:="linear_interpolation" \\
enable_gyro:=true \\
enable_accel:=true
rostopic list
/camera/accel/imu_info
/camera/align_to_color/parameter_descriptions
/camera/align_to_color/parameter_updates
/camera/aligned_depth_to_color/camera_info
/camera/aligned_depth_to_color/image_raw
/camera/aligned_depth_to_color/image_raw/compressed
/camera/aligned_depth_to_color/image_raw/compressed/parameter_descriptions
/camera/aligned_depth_to_color/image_raw/compressed/parameter_updates
/camera/aligned_depth_to_color/image_raw/compressedDepth
/camera/aligned_depth_to_color/image_raw/compressedDepth/parameter_descriptions
/camera/aligned_depth_to_color/image_raw/compressedDepth/parameter_updates
/camera/aligned_depth_to_color/image_raw/theora
/camera/aligned_depth_to_color/image_raw/theora/parameter_descriptions
/camera/aligned_depth_to_color/image_raw/theora/parameter_updates
/camera/color/camera_info
/camera/color/image_raw
/camera/color/image_raw/compressed
/camera/color/image_raw/compressed/parameter_descriptions
/camera/color/image_raw/compressed/parameter_updates
/camera/color/image_raw/compressedDepth
/camera/color/image_raw/compressedDepth/parameter_descriptions
/camera/color/image_raw/compressedDepth/parameter_updates
/camera/color/image_raw/theora
/camera/color/image_raw/theora/parameter_descriptions
/camera/color/image_raw/theora/parameter_updates
/camera/depth/camera_info
/camera/depth/image_rect_raw
/camera/depth/image_rect_raw/compressed
/camera/depth/image_rect_raw/compressed/parameter_descriptions
/camera/depth/image_rect_raw/compressed/parameter_updates
/camera/depth/image_rect_raw/compressedDepth
/camera/depth/image_rect_raw/compressedDepth/parameter_descriptions
/camera/depth/image_rect_raw/compressedDepth/parameter_updates
/camera/depth/image_rect_raw/theora
/camera/depth/image_rect_raw/theora/parameter_descriptions
/camera/depth/image_rect_raw/theora/parameter_updates
/camera/extrinsics/depth_to_color
/camera/gyro/imu_info
/camera/imu
/camera/l500_depth_sensor/parameter_descriptions
/camera/l500_depth_sensor/parameter_updates
/camera/motion_module/parameter_descriptions
/camera/motion_module/parameter_updates
/camera/realsense2_camera_manager/bond
/camera/rgb_camera/parameter_descriptions
/camera/rgb_camera/parameter_updates
/diagnostics
/rosout
/rosout_agg
/tf
/tf_static
就可以发现其将imu分成了两个：
“/camera/gyro/imu_info” 发布角速度
“/camera/accel/imu_info” 发布线加速度
但同时，也有
“/camera/imu”所以应该就不需要改动什么了。
同时还应该让imu与camera同步
roslaunch realsense2_camera rs_camera.launch \\
align_depth:=true \\
unite_imu_method:="linear_interpolation" \\
enable_gyro:=true \\
enable_accel:=true \\
enable_sync:=true然后就设置vins。首先在realsense_color_config.yaml基础上修改订阅的topic
#common parameters
# imu_topic: "/camera/imu/data_raw"
# image_topic: "/camera/color/image_raw"
imu_topic: "/camera/imu"
image_topic: "/camera/color/image_raw"相机的内参，通过读取camera_info得到或者自己标定，采用以下命令可以读取厂家的camera_info，但与实际可能存在差距。
rostopic echo /camera/color/camera_info
相机到IMU的变换矩阵
# 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.
#If you choose 0 or 1, you should write down the following matrix.
这里IMU和camera之间的外参矩阵建议使用Kalibr工具进行离线标定，也可以改成1或者2让估计器自己标定和优化。此处设定为2
realsense l515说是已经做好了硬件同步所以不需要在线估计同步时差（但是用kalibr标定出来和在线估计出来都存在大概-0.06的时间差）
#unsynchronization parameters
estimate_td: 0 # online estimate time offset between camera and imu
td: 0.000 # initial value of time offset. unit: s. readed image clock + td = real image clock (IMU clock)
相机曝光方式应为全局曝光
#rolling shutter parameters
rolling_shutter: 0 # 0: global shutter camera, 1: rolling shutter camera
rolling_shutter_tr: 0 # unit: s. rolling shutter read out time per frame (from data sheet).
然后就可以运行了
roslaunch realsense2_camera rs_camera.launch \\
align_depth:=true \\
unite_imu_method:="copy" \\
enable_gyro:=true \\
enable_accel:=true \\
enable_sync:=true
roslaunch vins_estimator realsense_color.launch
roslaunch vins_estimator vins_rviz.launch
好像不太行。。。换台电脑试试终于可以了。。。但是运动一段时间后，会飘得很严重，详见下面视频
vins
%YAML:1.0
#common parameters
imu_topic: "/camera/imu"
image_topic: "/camera/color/image_raw"
output_path: "/home/tony-ws1/output/"
#camera calibration
model_type: PINHOLE
camera_name: camera
image_width: 1280
image_height: 720
distortion_parameters:
k1: 9.2615504465028850e-02
k2: -1.8082438825995681e-01
p1: -6.5484100374765971e-04
p2: -3.5829351558557421e-04
projection_parameters:
fx: 6.0970550296798035e+02
fy: 6.0909579671294716e+02
cx: 3.1916667152289227e+02
cy: 2.3558360480225772e+02
# Extrinsic parameter between IMU and Camera.
estimate_extrinsic: 2 # 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.
#If you choose 0 or 1, you should write down the following matrix.
#Rotation from camera frame to imu frame, imu^R_cam
extrinsicRotation: !!opencv-matrix
rows: 3
cols: 3
dt: d
data: [ 0.99964621, 0.01105994, 0.02418954,
-0.01088975, 0.9999151, -0.00715601,
-0.02426663, 0.00689006, 0.99968178]
#Translation from camera frame to imu frame, imu^T_cam
extrinsicTranslation: !!opencv-matrix
rows: 3
cols: 1
dt: d
data: [0.07494282, -0.01077138, -0.00641822]
#feature traker paprameters
max_cnt: 150 # max feature number in feature tracking
min_dist: 25 # min distance between two features
freq: 10 # frequence (Hz) of publish tracking result. At least 10Hz for good estimation. If set 0, the frequence will be same as raw image
F_threshold: 1.0 # ransac threshold (pixel)
show_track: 1 # publish tracking image as topic
equalize: 0 # if image is too dark or light, trun on equalize to find enough features
fisheye: 0 # if using fisheye, trun on it. A circle mask will be loaded to remove edge noisy points
#optimization parameters
max_solver_time: 0.04 # max solver itration time (ms), to guarantee real time
max_num_iterations: 8 # max solver itrations, to guarantee real time
keyframe_parallax: 10.0 # keyframe selection threshold (pixel)
#imu parameters The more accurate parameters you provide, the better performance
acc_n: 0.1 # accelerometer measurement noise standard deviation. #0.2
gyr_n: 0.01 # gyroscope measurement noise standard deviation. #0.05
acc_w: 0.0002 # accelerometer bias random work noise standard deviation. #0.02
gyr_w: 2.0e-5 # gyroscope bias random work noise standard deviation. #4.0e-5
g_norm: 9.805 # gravity magnitude
#loop closure parameters
loop_closure: 1 # start loop closure
fast_relocalization: 1 # useful in real-time and large project
load_previous_pose_graph: 0 # load and reuse previous pose graph; load from 'pose_graph_save_path'
pose_graph_save_path: "/home/tony-ws1/output/pose_graph/" # save and load path
#unsynchronization parameters
estimate_td: 1 # online estimate time offset between camera and imu
td: 0.000 # initial value of time offset. unit: s. readed image clock + td = real image clock (IMU clock)
#rolling shutter parameters
rolling_shutter: 1 # 0: global shutter camera, 1: rolling shutter camera
rolling_shutter_tr: 0.033 # unit: s. rolling shutter read out time per frame (from data sheet).
#visualization parameters
save_image: 1 # save image in pose graph for visualization prupose; you can close this function by setting 0
visualize_imu_forward: 0 # output imu forward propogation to achieve low latency and high frequence results
visualize_camera_size: 0.4 # size of camera marker in RVIZ实现了之后发现还是会漂，感觉对相机进行矫正是非常有必要的，但是确又不知道各个参数的意义，终于在连接中找到了（https://github.com/HKPolyU-UAV/FLVIS）
header:
seq: 21
stamp:
secs: 1632650051
nsecs: 413622856
frame_id: "camera_color_optical_frame"
height: 720
width: 1280
distortion_model: "plumb_bob"
D: [0.14218834042549133, -0.48622673749923706, 0.0013360617449507117, 0.0007612911867909133, 0.444832980632782]
K: [905.8331298828125, 0.0, 649.684814453125, 0.0, 905.6519775390625, 352.701416015625, 0.0, 0.0, 1.0]
R: [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0]
P: [905.8331298828125, 0.0, 649.684814453125, 0.0, 0.0, 905.6519775390625, 352.701416015625, 0.0, 0.0, 0.0, 1.0, 0.0]
binning_x: 0
binning_y: 0
roi:
x_offset: 0
y_offset: 0
height: 0
width: 0
do_rectify: False
---
image_width: 1280 image_height: 720 cam0_intrinsics: [905.8331298828125, 905.6519775390625, 649.684814453125, 352.701416015625]#fx fy cx cy cam0_distortion_coeffs: [0.14218834042549133, -0.48622673749923706, 0.0013360617449507117, 0.0007612911867909133]#k1 k2 p1 p2
（所以对于镜头畸变一共有 5 个参数 k1, k2, k3, p1, p2 需要校准，这 5 个参数和 M1 一起，都是需要标定的相机内参。opencv 输出的即便参数顺序是 k1, k2, p1, p2, k3 因为 k3 没那么重要。）
采用标定的参数修改一下有：
%YAML:1.0
#common parameters
imu_topic: "/camera/imu"
image_topic: "/camera/color/image_raw"
output_path: "/home/tony-ws1/output/"
#camera calibration
model_type: PINHOLE
camera_name: camera
image_width: 1280
image_height: 720
distortion_parameters:
k1: 0.14218834042549133
k2: -0.48622673749923706
p1: 0.0013360617449507117
p2: 0.0007612911867909133
projection_parameters:
fx: 905.8331298828125
fy: 905.6519775390625
cx: 649.684814453125
cy: 352.701416015625
# Extrinsic parameter between IMU and Camera.
estimate_extrinsic: 2 # 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.
#If you choose 0 or 1, you should write down the following matrix.
#Rotation from camera frame to imu frame, imu^R_cam
extrinsicRotation: !!opencv-matrix
rows: 3
cols: 3
dt: d
data: [ 0.99964621, 0.01105994, 0.02418954,
-0.01088975, 0.9999151, -0.00715601,
-0.02426663, 0.00689006, 0.99968178]
#Translation from camera frame to imu frame, imu^T_cam
extrinsicTranslation: !!opencv-matrix
rows: 3
cols: 1
dt: d
data: [0.07494282, -0.01077138, -0.00641822]
#feature traker paprameters
max_cnt: 150 # max feature number in feature tracking
min_dist: 25 # min distance between two features
freq: 10 # frequence (Hz) of publish tracking result. At least 10Hz for good estimation. If set 0, the frequence will be same as raw image
F_threshold: 1.0 # ransac threshold (pixel)
show_track: 1 # publish tracking image as topic
equalize: 0 # if image is too dark or light, trun on equalize to find enough features
fisheye: 0 # if using fisheye, trun on it. A circle mask will be loaded to remove edge noisy points
#optimization parameters
max_solver_time: 0.04 # max solver itration time (ms), to guarantee real time
max_num_iterations: 8 # max solver itrations, to guarantee real time
keyframe_parallax: 10.0 # keyframe selection threshold (pixel)
#imu parameters The more accurate parameters you provide, the better performance
acc_n: 0.1 # accelerometer measurement noise standard deviation. #0.2
gyr_n: 0.01 # gyroscope measurement noise standard deviation. #0.05
acc_w: 0.0002 # accelerometer bias random work noise standard deviation. #0.02
gyr_w: 2.0e-5 # gyroscope bias random work noise standard deviation. #4.0e-5
g_norm: 9.805 # gravity magnitude
#loop closure parameters
loop_closure: 1 # start loop closure
fast_relocalization: 1 # useful in real-time and large project
load_previous_pose_graph: 0 # load and reuse previous pose graph; load from 'pose_graph_save_path'
pose_graph_save_path: "/home/tony-ws1/output/pose_graph/" # save and load path
#unsynchronization parameters
estimate_td: 1 # online estimate time offset between camera and imu
td: 0.000 # initial value of time offset. unit: s. readed image clock + td = real image clock (IMU clock)
#rolling shutter parameters
rolling_shutter: 1 # 0: global shutter camera, 1: rolling shutter camera
rolling_shutter_tr: 0.033 # unit: s. rolling shutter read out time per frame (from data sheet).
#visualization parameters
save_image: 1 # save image in pose graph for visualization prupose; you can close this function by setting 0
visualize_imu_forward: 0 # output imu forward propogation to achieve low latency and high frequence results
visualize_camera_size: 0.4 # size of camera marker in RVIZ好像还是不行。。。imu飘得好厉害
参考资料
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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——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——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
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——基于vscode的ros开发(代码片段)
在VSCODE中添加ROS的插件，可以使得其开发比较简单，但是有时候还是无法显示出一些ROS函数或者变量，修改.vscode文件夹下的c_cpp_properties.json文件如下:"configurations":["browse":"databaseFilename":"","li... 查看详情
ros实验笔记之——基于kalibr来标定davis346(代码片段)
之前博客《ROS学习笔记之——DAVIS346calibration》已经实现了用dv-gui（Calibration[Tutorial]·DV）来标定eventcamera了。但是缺少了跟IMU的外参标定等等。本博文利用Kalibr库来对其进行标定。Kalibr安装先创建一个工作空间mkdir-p~/kalibr_... 查看详情
ros实验笔记之——eventcamera产生ts与em(代码片段)
本博文是本人用eventcamera实现timesurface以及eventmap的记录FeatureExtraction参考：事件相机整理角点检测与跟踪总结|码农家园ESVO之前博客《ROS学习笔记之——ESVO复现及DAVIS346测试》已经介绍过ESVO了安装1.driver的安装，之前已经安装过... 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实验笔记之——slam无人驾驶初入门(代码片段)
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TheKITTIVisionBenchmarkSuitehttp://www.cvlibs.net/datasets/kitti/eva... 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eventcamera的asc*特征(代码片段)
目录原理测试参考资料：原理详细的理论部分就不阐述了，可以参考原文以及这个博客~【事件相机整理】角点检测与跟踪总结_larrydong的博客-CSDN博客测试先进入工程编译源码。注意用ros版本的～运行的节点图如下然... 查看详情
ros实验笔记之——安装qpep以及intel-mkl(代码片段)
最近打算尝试一下用QPEP代替opencv的PNP进位姿的估计。安装过程如下gitclonehttps://github.com/zarathustr/LibQPEPcdLibQPEPmkdirbuildcdbuildcmake..-DCMAKE_BUILD_TYPE=Releasesudomakeinstall会报错如下这是没有安装Intel-MKL导致的，安装的方法如下& 查看详情
ros实验笔记之——jcv-450无人机初入门
最近购买了阿木实验室的JCV-450无人机。本博文记录本人使用及实验的过程目录基本设置注意点飞行前调试QGC的使用关于遥控器，请参考下面的教程富斯i6s遥控器操作基本介绍基本设置首先开箱后发现里面的东西好像并不多。... 查看详情
ros实验笔记之——将节点中的计算结果保存到txt文件中(代码片段)
直接给出例程序吧#include<fstream>#include<iomanip>//为使用setw();//ROS_INFO("aftertheIMUPre-integrationoutputthePVQandbisas:Ps,Vs,Rs,Bas,Bgs");std::ofstreamout("/home/kwanwaipang/catkin_w 查看详情