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Ubuntu20.04LTS下安装Intel Realsense D435i驱动与ROS包
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文章目录
目标一、D435i简介二、环境配置三、RealSense的SDK2.0安装四、ROS包安装五、摄像机CV的ROS包节点
六、问题排查
目标
在Ubuntu20.04LTS系统下安装D435i的驱动SDK2和ROS包，实现在ROS环境中使用D435i。 一、D435i简介D435i是Inter公司RealSense系列摄像头的一种，采用英特尔最新的深度感应硬件和软件，封装成易于集成的产品。相比D435，D435i添加了IMU（惯性测量单元），不仅为 SLAM 和跟踪应用打开了大门，还使手持扫描系统的注册和校准更容易，在虚拟/增强现实和无人机等领域很重要。 尽管具有红外投射器，但并不是采用红外反射测距。它的作用仅仅是投射不可见的固定的红外纹理样式，提高在纹理不明显的环境中（例如白墙）的深度计算精度，辅助双目视觉测距。左右两个相机将图像数据送入内置的深度处理器，在其中基于双目测距的原理计算每个像素的深度值。 最高 1280×720 双目深度分辨率; 最高 1920×1080 RGB 分辨率; 最高 90 FPS 深度视频流。 深度流与普通 RGB 视频流类似，只不过每个像素点的值不再是 RGB 或灰度值，而是物体相对于相机的距离。 只能对相同帧率的 RGB 与深度视频流做同步设置。 双目 baseline 为 50 mm; 深度探测范围 0.2 m ~ 10 m; 深度坐标系以左侧相机为中心（下图中 centerline of 1/4-20 是指三脚架螺丝空的中心） 系统：Ubuntu20.04LTS ROS：1.15.14 摄像机：D435i 三、RealSense的SDK2.0安装方式一：(优先) 1.注册服务器的公钥 sudo apt-get update && sudo apt-get upgrade && sudo apt-get dist-upgrade sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-key F6E65AC044F831AC80A06380C8B3A55A6F3EFCDE || sudo apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv-key F6E65AC044F831AC80A06380C8B3A55A6F3EFCDE2.将服务器添加到存储库列表中 sudo add-apt-repository "deb https://librealsense.intel.com/Debian/apt-repo $(lsb_release -cs) main" -u3.安装SDK2 方式一（apt）： 基础安装 sudo apt-get install librealsense2-dkms sudo apt-get install librealsense2-utils选装 sudo apt-get install librealsense2-dev sudo apt-get install librealsense2-dbgTip:安装dev包后，可使用g++ -std=c++11 filename.cpp -lrealsense2 或 别的IDE来编译具有librealsense的应用。 测试安装结果 realsense-viewer
测试安装结果 realsense-viewer
方式一（apt）： 安装ROS版本的realsense2_camera sudo apt-get install ros-$ROS_DISTRO-realsense2-camera sudo apt-get install ros-$ROS_DISTRO-realsense2-description方式二（源代码）： 进入catkin_ws工作空间的src目录，下载realsense和ddynamic_reconfigure，然后用ROS的catkin_make指令编译。 cd ~/catkin_ws/src git clone https://github.com/IntelRealSense/realsense-ros.git git clone https://github.com/pal-robotics/ddynamic_reconfigure.git cd ~/catkin_ws && catkin_make安装rgbd-launch rgbd_launch是一组打开RGBD设备，并load 所有nodelets转化 raw depth/RGB/IR 流到深度图(depth image), 视差图(disparity image)和点云(point clouds)的launch文件集。 sudo apt-get install ros-noetic-rgbd-launch测试编译结果： roslaunch realsense2_camera demo_pointcloud.launch
在catkin_ws工作空间中创建一个usb_camera包，含有一个图像消息发布节点img_publisher与图像消息接收显示节点img_viewer。 package.xml： catkin cv_bridge image_transport roscpp sensor_msgs std_msgs opencv2 cv_bridge image_transport roscpp sensor_msgs std_msgs opencv2 cv_bridge image_transport roscpp sensor_msgs std_msgs opencv2img_publisher.cpp节点： #include #include #include #include #include #include int main(int argc, char** argv) { ros::init(argc, argv, "img_publisher"); ros::NodeHandle nh; image_transport::ImageTransport it(nh); image_transport::Publisher pub = it.advertise("camera/image", 1); cv::VideoCapture cap; cv::Mat frame; int deviceID=0; if(argc>1) deviceID=argv[1][0]-'0'; int apiID=cv::CAP_ANY; cap.open(deviceID+apiID); if(!cap.isOpened()){ std::cerr sensor_msgs::ImagePtr msg = cv_bridge::CvImage(std_msgs::Header(), "bgr8", frame).toImageMsg(); pub.publish(msg); } ros::spinOnce(); loop_rate.sleep(); } return 0; }img_viewer： //接收并查看图像消息的节点img_viewer.cpp #include #include #include #include //依赖包有std_msgs（消息传递），roscpp（c++），cv_bridge（ros和opencv图像消息转换），sensor_msgs（传感器消息），image_transport（图像编码传输） void imageCallback(const sensor_msgs::ImageConstPtr& msg) { try { cv::imshow("view", cv_bridge::toCvShare(msg, "bgr8")->image); } catch (cv_bridge::Exception& e) { ROS_ERROR("Could not convert from '%s' to 'bgr8'.", msg->encoding.c_str()); } } int main(int argc, char **argv) { ros::init(argc, argv, "img_viewer"); ros::NodeHandle nh; cv::namedWindow("view"); cv::startWindowThread(); image_transport::ImageTransport it(nh); image_transport::Subscriber sub = it.subscribe("camera/image", 1, imageCallback); ros::spin(); cv::destroyWindow("view"); return 0; }CMakeLists.txt： cmake_minimum_required(VERSION 3.0.2) project(usb_camera) ## Compile as C++11, supported in ROS Kinetic and newer # add_compile_options(-std=c++11) ## Find catkin macros and libraries ## if COMPONENTS list like find_package(catkin REQUIRED COMPONENTS xyz) ## is used, also find other catkin packages find_package(catkin REQUIRED COMPONENTS cv_bridge image_transport roscpp sensor_msgs std_msgs ) ## System dependencies are found with CMake's conventions # find_package(Boost REQUIRED COMPONENTS system) find_package(OpenCV REQUIRED) ## Uncomment this if the package has a setup.py. This macro ensures ## modules and global scripts declared therein get installed ## See http://ros.org/doc/api/catkin/html/user_guide/setup_dot_py.html # catkin_python_setup() ################################################ ## Declare ROS messages, services and actions ## ################################################ ## To declare and build messages, services or actions from within this ## package, follow these steps: ## * Let MSG_DEP_SET be the set of packages whose message types you use in ## your messages/services/actions (e.g. std_msgs, actionlib_msgs, ...). ## * In the file package.xml: ## * add a build_depend tag for "message_generation" ## * add a build_depend and a exec_depend tag for each package in MSG_DEP_SET ## * If MSG_DEP_SET isn't empty the following dependency has been pulled in ## but can be declared for certainty nonetheless: ## * add a exec_depend tag for "message_runtime" ## * In this file (CMakeLists.txt): ## * add "message_generation" and every package in MSG_DEP_SET to ## find_package(catkin REQUIRED COMPONENTS ...) ## * add "message_runtime" and every package in MSG_DEP_SET to ## catkin_package(CATKIN_DEPENDS ...) ## * uncomment the add_*_files sections below as needed ## and list every .msg/.srv/.action file to be processed ## * uncomment the generate_messages entry below ## * add every package in MSG_DEP_SET to generate_messages(DEPENDENCIES ...) ## Generate messages in the 'msg' folder # add_message_files( # FILES # Message1.msg # Message2.msg # ) ## Generate services in the 'srv' folder # add_service_files( # FILES # Service1.srv # Service2.srv # ) ## Generate actions in the 'action' folder # add_action_files( # FILES # Action1.action # Action2.action # ) ## Generate added messages and services with any dependencies listed here # generate_messages( # DEPENDENCIES # sensor_msgs# std_msgs # ) ################################################ ## Declare ROS dynamic reconfigure parameters ## ################################################ ## To declare and build dynamic reconfigure parameters within this ## package, follow these steps: ## * In the file package.xml: ## * add a build_depend and a exec_depend tag for "dynamic_reconfigure" ## * In this file (CMakeLists.txt): ## * add "dynamic_reconfigure" to ## find_package(catkin REQUIRED COMPONENTS ...) ## * uncomment the "generate_dynamic_reconfigure_options" section below ## and list every .cfg file to be processed ## Generate dynamic reconfigure parameters in the 'cfg' folder # generate_dynamic_reconfigure_options( # cfg/DynReconf1.cfg # cfg/DynReconf2.cfg # ) ################################### ## catkin specific configuration ## ################################### ## The catkin_package macro generates cmake config files for your package ## Declare things to be passed to dependent projects ## INCLUDE_DIRS: uncomment this if your package contains header files ## LIBRARIES: libraries you create in this project that dependent projects also need ## CATKIN_DEPENDS: catkin_packages dependent projects also need ## DEPENDS: system dependencies of this project that dependent projects also need catkin_package( # INCLUDE_DIRS include # LIBRARIES usb_camera # CATKIN_DEPENDS cv_bridge image_transport roscpp sensor_msgs std_msgs # DEPENDS system_lib ) ########### ## Build ## ########### ## Specify additional locations of header files ## Your package locations should be listed before other locations include_directories( # include ${catkin_INCLUDE_DIRS} ${OpenCV_INCLUDE_DIRS} ) ## Declare a C++ library # add_library(${PROJECT_NAME} # src/${PROJECT_NAME}/usb_camera.cpp # ) ## Add cmake target dependencies of the library ## as an example, code may need to be generated before libraries ## either from message generation or dynamic reconfigure # add_dependencies(${PROJECT_NAME} ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS}) ## Declare a C++ executable ## With catkin_make all packages are built within a single CMake context ## The recommended prefix ensures that target names across packages don't collide # add_executable(${PROJECT_NAME}_node src/usb_camera_node.cpp) ## Rename C++ executable without prefix ## The above recommended prefix causes long target names, the following renames the ## target back to the shorter version for ease of user use ## e.g. "rosrun someones_pkg node" instead of "rosrun someones_pkg someones_pkg_node" # set_target_properties(${PROJECT_NAME}_node PROPERTIES OUTPUT_NAME node PREFIX "") ## Add cmake target dependencies of the executable ## same as for the library above # add_dependencies(${PROJECT_NAME}_node ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS}) ## Specify libraries to link a library or executable target against # target_link_libraries(${PROJECT_NAME}_node # ${catkin_LIBRARIES} # ) add_executable(img_publisher src/img_publisher.cpp) target_link_libraries(img_publisher ${catkin_LIBRARIES} ${OpenCV_LIBS}) add_executable(img_viewer src/img_viewer.cpp) target_link_libraries(img_viewer ${catkin_LIBRARIES} ${OpenCV_LIBS}) ############# ## Install ## ############# # all install targets should use catkin DESTINATION variables # See http://ros.org/doc/api/catkin/html/adv_user_guide/variables.html ## Mark executable scripts (Python etc.) for installation ## in contrast to setup.py, you can choose the destination # catkin_install_python(PROGRAMS # scripts/my_python_script # DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION} # ) ## Mark executables for installation ## See http://docs.ros.org/melodic/api/catkin/html/howto/format1/building_executables.html # install(TARGETS ${PROJECT_NAME}_node # RUNTIME DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION} # ) ## Mark libraries for installation ## See http://docs.ros.org/melodic/api/catkin/html/howto/format1/building_libraries.html # install(TARGETS ${PROJECT_NAME} # ARCHIVE DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION} # LIBRARY DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION} # RUNTIME DESTINATION ${CATKIN_GLOBAL_BIN_DESTINATION} # ) ## Mark cpp header files for installation # install(DIRECTORY include/${PROJECT_NAME}/ # DESTINATION ${CATKIN_PACKAGE_INCLUDE_DESTINATION} # FILES_MATCHING PATTERN "*.h" # PATTERN ".svn" EXCLUDE # ) ## Mark other files for installation (e.g. launch and bag files, etc.) # install(FILES # # myfile1 # # myfile2 # DESTINATION ${CATKIN_PACKAGE_SHARE_DESTINATION} # ) ############# ## Testing ## ############# ## Add gtest based cpp test target and link libraries # catkin_add_gtest(${PROJECT_NAME}-test test/test_usb_camera.cpp) # if(TARGET ${PROJECT_NAME}-test) # target_link_libraries(${PROJECT_NAME}-test ${PROJECT_NAME}) # endif() ## Add folders to be run by python nosetests # catkin_add_nosetests(test)
1. 查找usb摄像头 在最初的节点运行时，提示找不到usb摄像头，改变摄像机的索引号也不行。查摄像头占用情况： ls /dev/video* /dev/video0 /dev/video1 /dev/video2 /dev/video3 /dev/video4 /dev/video5摄像机索引没问题。 根据输出信息[ WARN:0] OpenCV | GStreamer warning: Embedded video playback halted; module v4l2src0 reported: Cannot identify device '/dev/video0'. [ WARN:0] OpenCV | GStreamer warning: unable to start pipeline [ WARN:0] OpenCV | GStreamer warning: GStreamer: pipeline have not been created 查找相关文献，试探性的调试如下： find /lib/modules/ -name "*v4l2*.ko"显示如下信息： /lib/modules/5.13.0-30-generic/kernel/drivers/media/v4l2-core/v4l2-flash-led-class.ko /lib/modules/5.13.0-30-generic/kernel/drivers/media/v4l2-core/v4l2-dv-timings.ko /lib/modules/5.13.0-30-generic/kernel/drivers/media/v4l2-core/v4l2-fwnode.ko /lib/modules/5.13.0-30-generic/kernel/drivers/media/v4l2-core/v4l2-mem2mem.ko /lib/modules/5.13.0-30-generic/kernel/drivers/media/i2c/adv7511-v4l2.ko /lib/modules/5.13.0-30-generic/kernel/drivers/media/common/videobuf2/videobuf2-v4l2.ko /lib/modules/5.13.0-30-generic/kernel/drivers/media/common/v4l2-tpg/v4l2-tpg.ko /lib/modules/5.13.0-27-generic/kernel/drivers/media/v4l2-core/v4l2-flash-led-class.ko /lib/modules/5.13.0-27-generic/kernel/drivers/media/v4l2-core/v4l2-dv-timings.ko /lib/modules/5.13.0-27-generic/kernel/drivers/media/v4l2-core/v4l2-fwnode.ko /lib/modules/5.13.0-27-generic/kernel/drivers/media/v4l2-core/v4l2-mem2mem.ko /lib/modules/5.13.0-27-generic/kernel/drivers/media/i2c/adv7511-v4l2.ko /lib/modules/5.13.0-27-generic/kernel/drivers/media/common/videobuf2/videobuf2-v4l2.ko /lib/modules/5.13.0-27-generic/kernel/drivers/media/common/v4l2-tpg/v4l2-tpg.ko发现相关的ko，其中，adv7511-v4l2.ko这个是与usb摄像机相关的文件。运行： sudo modprobe adv7511-v4l2然后就可以打开USB摄像机了。modprobe命令用于自动处理可载入模块，设置摄像头设备后相当于默认使用该设备。 另外，用v4l2-ctl --list-devices 命令可以查看摄像头对应的设备号，如果没有v4l2-ctl，可先安装： sudo apt install v4l-utils v4l2-ctl --list-devices Video Capture 9 (usb-0000:00:14.0-3): /dev/video0 /dev/video1 /dev/video2 /dev/video3 /dev/video4 /dev/video5 /dev/media0 /dev/media1经过上述操作后并不一定完全解决问题。 2. 了解Gstreamer框架 gstreamer是一个开发流式多媒体应用的开源框架，是为更好的处理音视频而设计开发的，但又不限于处理音视频，只要是有明显的数据流特征的应用。 gstreamer的功能核心是pipeline框架以及用于扩展功能的插件。通过gstreamer多媒体数据协商机制和同步机制，pipeline能够很好的地处理流式数据。开发者可以灵活利用已有插件，而且还可以自定义特定功能插件。 [1] Ubantu20.04安装libRealsense SDK 2.0 和ROS Wrapper for Intel RealSense Devices [2] Ubuntu20.04+RealSense D435i [3] ubuntu opencv 打开摄像头失败 [4] Gstreamer基础知识介绍 |
D435i是一个RGB-D相机，也就是我们常说的深度相机，目前深度相机的实现原理主要分为三种：分别是结构光，Tof，双目成像。REALSENSE使用的是结构光的方案。正面的四个摄像头，从左向右以次是左红外相机，红外点阵投射仪，右红外相机，和RGB相机。
方式二（源代码）： 下载librealsense，然后进入该目录，运行下列指令安装和编译依赖项：
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