使用VSCode调试Apollo无人车代码

　　本文链接地址:  使用VSCode 调试Apollo无人车代码 - 慢慢的回味
　　深入研究Apollo的代码是学习自动驾驶的很好途径。很多前沿科技，比如图像识别，激光雷达，多传感器融合，路径规划都可以直接完整地学习。能够直接调试代码是比读代码更能加深理解的。本文就介绍怎么去调试Apollo的代码。
　　Content: 下载代码 安装Docker 安装无线网卡驱动（可选） 安装Nvidia驱动 安装Nvidia Docker 启动Apollo项目 调试一个车道线识别程序 下载代码
　　系统使用Ubuntu 18.04版本。
　　https://gitee.com/ApolloAuto/apollo.git
　　比如目录为~/apollo。 安装Dockercurl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /usr/share/keyrings/docker-archive-keyring.gpg echo   ＂deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] https://download.docker.com/linux/ubuntu  $(lsb_release -cs) stable＂ | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null   sudo apt-get update sudo apt-get install docker-ce docker-ce-cli containerd.io systemctl daemon-reload systemctl restart docker安装无线网卡驱动（可选）
　　下载驱动：https://codeload.github.com/gnab/rtl8812au/zip/refs/heads/master make dkms_install echo 8812au | sudo tee -a /etc/modules insmod 8812au.ko安装Nvidia驱动
　　本人的显卡为GTX1060。Apollo项目需要Nvidia显卡，否则大部分模块无法编译。 sudo apt-get install linux-headers-$(uname -r) distribution=$(. /etc/os-release;echo $ID$VERSION_ID | sed -e ＂s/.//g＂) wget https://developer.download.nvidia.com/compute/cuda/repos/$distribution/x86_64/cuda-$distribution.pin sudo mv cuda-$distribution.pin /etc/apt/preferences.d/cuda-repository-pin-600 sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/$distribution/x86_64/7fa2af80.pub echo ＂deb http://developer.download.nvidia.com/compute/cuda/repos/$distribution/x86_64 /＂ | sudo tee /etc/apt/sources.list.d/cuda.list sudo apt-get update sudo apt-get -y install cuda-drivers export PATH=$PATH:/usr/local/cuda-11.2/bin export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-11.2/lib64:/usr/local/cuda/cuda/lib64
　　安装完成后，如下命令可以查看显卡信息。 nvidia-smi安装Nvidia Dockerdistribution=$(. /etc/os-release;echo $ID$VERSION_ID)    && curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -    && curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list sudo apt-get update sudo apt-get install -y nvidia-docker2 sudo systemctl restart docker
　　安装完成后，可启动示例Dockers程序查看。 sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi启动Apollo项目
　　在源码目录~/apollo中，执行如下命令，进入Apollo Docker： ./docker/scripts/dev_start.sh  ./docker/scripts/dev_into.sh
　　以后进入，直接启动已有的Docker即可： docker ps -a docker start 671567b64765 ./docker/scripts/dev_into.sh调试一个车道线识别程序
　　创建一个Bazel编译配置：
　　~/apollo/modules/perception/camera/test/BUILD
　　内容为： load(＂@rules_cc//cc:defs.bzl＂, ＂cc_library＂, ＂cc_test＂) load(＂//tools:cpplint.bzl＂, ＂cpplint＂)   package(default_visibility = [＂//visibility:public＂])   cc_test(     name = ＂camera_lib_lane_detector_darkscnn_lane_detector_test＂,     size = ＂medium＂,     srcs = [＂camera_lib_lane_detector_darkscnn_lane_detector_test.cc＂],     deps = [         ＂//cyber＂,         ＂//modules/perception/base＂,         ＂//modules/perception/camera/lib/lane/detector/darkSCNN:darkSCNN_lane_detector＂,         ＂//modules/perception/common/io:io_util＂,         ＂@com_google_googletest//:gtest_main＂,         ＂@opencv//:core＂,     ], )   cpplint()
　　现在可以编译Apollo程序了： ./apollo.sh build_dbg
　　启动GDB Server，供Docker外面的VS Code进行远程调试： export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/libtorch_gpu/lib/ apt update apt install gdbserver gdbserver 127.0.0.1:2222 bazel-bin/modules/perception/camera/test/camera_lib_lane_detector_darkscnn_lane_detector_test
　　启动VS Code，安装C++插件，然后点击Debug，添加如下配置即可调试Docker里面刚才启动的程序：(注意修改program的路径) {     ＂version＂: ＂0.2.0＂,     ＂configurations＂: [         {             ＂name＂: ＂gdb Remote camera_lib_lane_postprocessor_darkscnn_lane_postprocessor_test＂,             ＂type＂: ＂cppdbg＂,             ＂request＂: ＂launch＂,             ＂program＂: ＂~/apollo/.cache/bazel/540135163923dd7d5820f3ee4b306b32/execroot/apollo/bazel-out/k8-dbg/bin/modules/perception/camera/test/camera_lib_lane_detector_darkscnn_lane_detector_test＂,             ＂args＂: [＂myarg1＂, ＂myarg2＂, ＂myarg3＂],             ＂stopAtEntry＂: true,             ＂environment＂: [],             ＂externalConsole＂: false,             ＂MIMode＂: ＂gdb＂,             ＂miDebuggerPath＂: ＂gdb＂,             ＂miDebuggerArgs＂: ＂gdb＂,             ＂linux＂: {                 ＂MIMode＂: ＂gdb＂,                 ＂miDebuggerPath＂: ＂/usr/bin/gdb＂,                 ＂miDebuggerServerAddress＂: ＂127.0.0.1:2222＂,             },             ＂logging＂: {                 ＂moduleLoad＂: false,                 ＂engineLogging＂: false,                 ＂trace＂: false             },             ＂setupCommands＂: [                 {                     ＂description＂: ＂Enable pretty-printing for gdb＂,                     ＂text＂: ＂-enable-pretty-printing＂,                     ＂ignoreFailures＂: true                 }             ],             ＂cwd＂: ＂${workspaceFolder}＂,         }     ] }