Autoware.universe 安装步骤

克隆 Autoware 到本地

mkdir autoware_universe
cd autoware_universe/
git clone https://github.com/autowarefoundation/autoware.git -b humble

				

					
				
				

					
				
				

					
				
				

					
				
			

注意 ROS 的版本

安装 NVIDIA 显卡驱动

若输入 sudo nvidia-smi 检查英伟达驱动出现以下报错：

Failed to initialize NVML: Driver/library version mismatch
NVML library version: 550.90  

				

					
				
				

					
				
				

					
				
				

					
				
			

原因是 NVIDIA 内核驱动与系统驱动版本不一致，按照网上提出的方法使用 sudo rmmod nvidia 命令退出当前内核使用的显卡模块，重新加载升级后版本的显卡驱动作为我们的内核模块发现此方法不可行

最后解决办法为：

卸载驱动

sudo apt-get purge nvidia*

				

					
				
				

					
				
				

					
				
				

					
				
			

查找本机内核版本

cat /proc/driver/nvidia/version

				

					
				
				

					
				
				

					
				
				

					
				
			

发现为 550.90.07

于是尝试安装 550 版本的驱动

sudo apt-get install nvidia-driver-550 nvidia-settings nvidia-prime

				

					
				
				

					
				
				

					
				
				

					
				
			

出现报错

正在解压 libnvidia-compute-550:amd64 (550.90.07-0ubuntu1) 并覆盖 (550.90.07-0ubuntu0.22.04.1) ...
dpkg: 处理归档 /tmp/apt-dpkg-install-WqT5B5/04-libnvidia-compute-550_550.90.07-0ubuntu1_amd64.deb (--unpack)时出错：
正试图覆盖 /usr/lib/x86_64-linux-gnu/libnvidia-gpucomp.so.550.90.07，它同时被包含于软件包 libnvidia-gl-550:amd64 550.90.07-0ubuntu0.22.04.1
dpkg-deb: 错误: 粘贴 子进程被信号(Broken pipe) 终止了

				

					
				
				

					
				
				

					
				
				

					
				
			

具体是 libnvidia-compute-550 和 libnvidia-gl-550 包在更新时发生了冲突

强制卸载冲突的包

sudo dpkg --remove --force-remove-reinstreq libnvidia-compute-550 libnvidia-compute-550:i386
sudo dpkg --remove --force-remove-reinstreq libnvidia-gl-550 libnvidia-gl-550:i386

				

					
				
				

					
				
				

					
				
				

					
				
			

清理已损坏的包和未完成的安装：

sudo apt-get clean
sudo apt-get autoremove
sudo apt-get autoclean
sudo apt-get -f install

				

					
				
				

					
				
				

					
				
				

					
				
			

重新安装 NVIDIA 驱动

sudo apt-get install nvidia-driver-550 nvidia-settings nvidia-prime

				

					
				
				

					
				
				

					
				
				

					
				
			

重新生成内核模块

sudo dkms autoinstall

				

					
				
				

					
				
				

					
				
				

					
				
			

重启系统以应用更改

sudo reboot

				

					
				
				

					
				
				

					
				
				

					
				
			

再次使用 nvidia-smi 检查驱动显示驱动正常

安装 ROS2 humble

鱼香 ros 一键安装：

wget http://fishros.com/install -O fishros && . fishros

				

					
				
				

					
				
				

					
				
				

					
				
			

安装 ros2_dev_tools

sudo apt update && sudo apt install -y \
  build-essential \
  cmake \
  git \
  python3-colcon-common-extensions \
  python3-flake8 \
  python3-pip \
  python3-pytest-cov \
  python3-rosdep \
  python3-setuptools \
  python3-vcstool \
  wget

				

					
				
				

					
				
				

					
				
				

					
				
			

python3 -m pip install -U \
  flake8-blind-except \
  flake8-builtins \
  flake8-class-newline \
  flake8-comprehensions \
  flake8-deprecated \
  flake8-docstrings \
  flake8-import-order \
  flake8-quotes \
  pytest-repeat \
  pytest-rerunfailures \
  pytest \
  setuptools

				

					
				
				

					
				
				

					
				
				

					
				
			

安装 Ansible

sudo apt-get purge ansible
sudo apt-get -y update
sudo apt-get -y install pipx
python3 -m pipx ensurepath
pipx install --include-deps --force "ansible==6.*"
cd autoware/
ansible-galaxy collection install -f -r "ansible-galaxy-requirements.yaml"

				

					
				
				

					
				
				

					
				
				

					
				
			

安装 Build Tools

sudo apt-get update
sudo apt-get install -y ccache

				

					
				
				

					
				
				

					
				
				

					
				
			

安装 Dev Tools

sudo apt-get install -y git-lfs
git lfs install
pip3 install pre-commit
pip3 install clang-format==18.1.8 --user --extra-index-url=https://pypi.tuna.tsinghua.edu.cn/simple
sudo apt-get install -y golang
sudo apt-get install -y ros-${ROS_DISTRO}-plotjuggler-ros

				

					
				
				

					
				
				

					
				
				

					
				
			

安装 gdown

pip3 install gdown

				

					
				
				

					
				
				

					
				
				

					
				
			

安装 geographiclib

sudo apt install geographiclib-tools
sudo geographiclib-get-geoids egm2008-1

				

					
				
				

					
				
				

					
				
				

					
				
			

安装 rmw_implementation

# 注意官网给的是mian，humble的
wget -O /tmp/amd64.env https://raw.githubusercontent.com/autowarefoundation/autoware/main/amd64.env && source /tmp/amd64.env
sudo apt update
rmw_implementation_dashed=$(eval sed -e "s/_/-/g" <<< "${rmw_implementation}")
sudo apt install ros-${rosdistro}-${rmw_implementation_dashed}
echo '' >> ~/.bashrc && echo "export RMW_IMPLEMENTATION=${rmw_implementation}" >> ~/.bashrc

				

					
				
				

					
				
				

					
				
				

					
				
			

安装 pacmod

wget -O /tmp/amd64.env https://raw.githubusercontent.com/autowarefoundation/autoware/galactic/amd64.env && source /tmp/amd64.env
sudo apt install apt-transport-https
sudo sh -c 'echo "deb [trusted=yes] https://s3.amazonaws.com/autonomoustuff-repo/ $(lsb_release -sc) main" > /etc/apt/sources.list.d/autonomoustuff-public.list'
sudo apt update
sudo apt install ros-${rosdistro}-pacmod3

				

					
				
				

					
				
				

					
				
				

					
				
			

安装 autoware_core

pip3 install gdown

				

					
				
				

					
				
				

					
				
				

					
				
			

安装 autoware universe dependencies

sudo apt install geographiclib-tools
sudo geographiclib-get-geoids egm2008-1

				

					
				
				

					
				
				

					
				
				

					
				
			

安装 pre_commit

clang_format_version=14.0.6
pip3 install pre-commit clang-format==${clang_format_version}
sudo add-apt-repository ppa:longsleep/golang-backports
sudo apt install golang

				

					
				
				

					
				
				

					
				
				

					
				
			

安装 CUDA

查看 amd64.env, 可以看到推荐的 CUDA 版本为 12.3，在 https://developer.nvidia.com/cuda-toolkit 官网中选择版本，为了避免因为 CUDA 版本太高，cuDNN、TensorRT 无法使用，故下载 CUDA12.0
网页中选择好 CUDA 版本安装 deb 方式安装：

wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.0-1_all.deb
sudo dpkg -i cuda-keyring_1.0-1_all.deb
sudo apt-get update
sudo apt-get -y install cuda-toolkit-12-0

				

					
				
				

					
				
				

					
				
				

					
				
			

设置环境变量

export PATH=/usr/local/cuda-12.0/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-12.0/lib64:$LD_LIBRARY_PATH

				

					
				
				

					
				
				

					
				
				

					
				
			

检查 CUDA 版本：

nvcc --version

				

					
				
				

					
				
				

					
				
				

					
				
			

安装 cuDNN

在官网 https://developer.nvidia.com/rdp/cudnn-archive 下载好 cuDNN v8.9.5 for CUDA12.X 后，解压文件

tar -xvf cudnn-linux-x86_64-8.9.5.30_cuda12-archive.tar.xz 

				

					
				
				

					
				
				

					
				
				

					
				
			

将解压后的头文件和库复制到 cuda 目录中：

cd cudnn-linux-x86_64-8.9.5.30_cuda12-archive/
sudo cp include/cudnn*    /usr/local/cuda/include 
sudo cp lib/libcudnn*    /usr/local/cuda/lib64 
sudo chmod a+r /usr/local/cuda/include/cudnn*   /usr/local/cuda/lib64/libcudnn*

				

					
				
				

					
				
				

					
				
				

					
				
			

cuDNN 安装完成，查看安装的版本：

cat /usr/local/cuda/include/cudnn_version.h | grep CUDNN_MAJOR -A 2

				

					
				
				

					
				
				

					
				
				

					
				
			

安装 TensorRT

在官网 https://developer.nvidia.com/nvidia-tensorrt-8x-download 选择 TensorRT 8.6 进行下载后解压：

sudo dpkg -i nv-tensorrt-*.deb

				

					
				
				

					
				
				

					
				
				

					
				
			

配置环境变量：将 TensorRT 的库路径添加到 LD_LIBRARY_PATH 中，编辑～/.bashrc 文件并添加以下内容：

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/lib/x86_64-linux-gnu/

				

					
				
				

					
				
				

					
				
				

					
				
			

重启终端：保存～/.bashrc 文件并执行以下命令使配置生效：

source ~/.bashrc

				

					
				
				

					
				
				

					
				
				

					
				
			

安装完成后，您可以通过运行以下命令验证 TensorRT 是否已成功安装：

dpkg -l | grep TensorRT

				

					
				
				

					
				
				

					
				
				

					
				
			

下载 Autoware artifacts

cd autoware/
ansible-galaxy collection install -f -r "ansible-galaxy-requirements.yaml"
ansible-playbook autoware.dev_env.download_artifacts -e "data_dir=$HOME/autoware_data" --ask-become-pass

				

					
				
				

					
				
				

					
				
				

					
				
			

编译源码

克隆存储库：

cd autoware
mkdir src
vcs import src < autoware.repos

				

					
				
				

					
				
				

					
				
				

					
				
			

安装依赖的 ROS 包

sudo rosdep init
sudo rosdep update
rosdepc install
source /opt/ros/humble/setup.bash
rosdepc install -y --from-paths src --ignore-src --rosdistro $ROS_DISTRO

				

					
				
				

					
				
				

					
				
				

					
				
			

编译：

colcon build --symlink-install --cmake-args -DCMAKE_BUILD_TYPE=Release

				

					
				
				

					
				
				

					
				
				

					
				
			

单独编译某个包：

colcon build --packages-up-to <name-of-pkg>

				

					
				
				

					
				
				

					
				
				

					
				
			

清理并重新构建：有时候构建过程中的残留文件可能会导致问题。尝试清理以前的构建文件并重新构建项目：

rm -rf build install log
colcon clean

				

					
				
				

					
				
				

					
				
				

					
				
			

编译报错

/home/buaa/TensorRT-8.6.1.6/include/NvInferRuntime.h:674:61: error: unused parameter ‘pluginFactory’ [-Werror=unused-parameter]
674 | void const blob, std::size_t size, IPluginFactory pluginFactory) noexcept
| ~~~~^~~~~
cc1plus: all warnings being treated as errors
gmake[2]: [CMakeFiles/tensorrt_common.dir/build.make:76: CMakeFiles/tensorrt_common.dir/src/tensorrt_common.cpp.o] Error 1
gmake[1]: [CMakeFiles/Makefile2:137: CMakeFiles/tensorrt_common.dir/all] Error 2
gmake: *** [Makefile:146: all] Error 2

这个错误主要是由于编译器将所有警告都视为错误，而 NvInferRuntime.h 中的一个未使用的参数 pluginFactory 触发了这个警告

解决方法：忽略未使用参数的警告

找到包含 tensorrt_common 包的 CMakeLists.txt 文件：

find . -name "CMakeLists.txt" | grep tensorrt_common

				

					
				
				

					
				
				

					
				
				

					
				
			

终端输出：

./src/universe/autoware.universe/common/tensorrt_common/CMakeLists.txt

				

					
				
				

					
				
				

					
				
				

					
				
			

修改 CMakeLists.txt 文件，在 CMakeLists.txt 文件中添加以下行，忽略未使用参数的警告：

set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-error=unused-parameter")

				

					
				
				

					
				
				

					
				
				

					
				
			

出现警告

CMake Warning at CMakeLists.txt:20 (message):
cuda, cudnn, tensorrt libraries are not found

解决方法 设置环境变量：

export PATH=/usr/local/cuda/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
source ~/.bashrc

				

					
				
				

					
				
				

					
				
				

					
				
			

编译警告 1：

--- stderr: elevation_map_loader
CMake Warning at CMakeLists.txt:16 (find_package):
By not providing "Findrosbag2_storage_sqlite3.cmake" in CMAKE_MODULE_PATH
this project has asked CMake to find a package configuration file provided
by "rosbag2_storage_sqlite3", but CMake did not find one.
Could not find a package configuration file provided by
"rosbag2_storage_sqlite3" with any of the following names:
rosbag2_storage_sqlite3Config.cmake
rosbag2_storage_sqlite3-config.cmake
Add the installation prefix of "rosbag2_storage_sqlite3" to
CMAKE_PREFIX_PATH or set "rosbag2_storage_sqlite3_DIR" to a directory
containing one of the above files. If "rosbag2_storage_sqlite3" provides a
separate development package or SDK, be sure it has been installed.

解决办法：安装对应的包

sudo apt-get install ros-$ROS_DISTRO-rosbag2-storage*

				

					
				
				

					
				
				

					
				
				

					
				
			

编译警告 2：
--- stderr: yabloc_pose_initializer
CMake Warning (dev) at CMakeLists.txt:12 (find_package):
Policy CMP0074 is not set: find_package uses _ROOT variables.
Run "cmake --help-policy CMP0074" for policy details. Use the cmake_policy
command to set the policy and suppress this warning.
CMake variable PCL_ROOT is set to:
/usr
For compatibility, CMake is ignoring the variable.
This warning is for project developers. Use -Wno-dev to suppress it.

---

解决办法：安装对应的包

CMP0074 未设置，而在项目中使用了 find_package 命令，该命令使用了 PCL_ROOT 变量。
在对应的包的 project () 命令之后，find_package () 命令之前，添加

cmake_policy(SET CMP0074 NEW)

				

					
				
				

					
				
				

					
				
				

					
				
			

使用以下命令来查找功能包所在的位置，例如要寻找上面报错的 yabloc_pose_initializer 包的位置：

colcon list | grep yabloc_pose_initializer

				

					
				
				

					
				
				

					
				
				

					
				
			

然后单独编译这个包：

colcon build --packages-up-to yabloc_pose_initializer

				

					
				
				

					
				
				

					
				
				

					
				
			

编译警告 3

--- stderr: bag_time_manager_rviz_plugin
CMake Warning (dev) at CMakeLists.txt:7 (find_package):
Ignoring EXACT since no version is requested.
This warning is for project developers. Use -Wno-dev to suppress it.

---

解决办法：

没安装 QT5 或者没在～/.bashrc 中指明 QT5 的位置，查询 QT5 是否安装，若安装则或打印出安装位置:

qmake --version

				

					
				
				

					
				
				

					
				
				

					
				
			

终端输出：

QMake version 3.1
Using Qt version 5.15.3 in /usr/lib/x86_64-linux-gnu

故在～/.bashrc 添加:

export PATH="/usr/lib/x86_64-linux-gnu/qt5/bin:$PATH"
export LD_LIBRARY_PATH="/usr/lib/x86_64-linux-gnu/qt5/lib:$LD_LIBRARY_PATH"

				

					
				
				

					
				
				

					
				
				

					
				
			

安装 Autoware Build GUI

安装依赖：

sudo apt install libwebkit2gtk-4.1-0 libjavascriptcoregtk-4.1-0 libsoup-3.0-0 libsoup-3.0-common

				

					
				
				

					
				
				

					
				
				

					
				
			

安装 Rust

curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs/ | sh
sudo apt install rustc

				

					
				
				

					
				
				

					
				
				

					
				
			

验证:

rustc --version

				

					
				
				

					
				
				

					
				
				

					
				
			

安装 Node.js：

sudo apt install nodejs

				

					
				
				

					
				
				

					
				
				

					
				
			

验证 Node.js 安装

node --version

				

					
				
				

					
				
				

					
				
				

					
				
			

安装 npm

npm install -g pnpm

				

					
				
				

					
				
				

					
				
				

					
				
			

下载源码：

git clone https://github.com/leo-drive/autoware-build-gui.git

				

					
				
				

					
				
				

					
				
				

					
				
			

安装.deb

sudo dpkg -i autoware-build-gui_1.0.3_amd64.deb

				

					
				
				

					
				
				

					
				
				

					
				
			

下载好高精地图后就可运行案例

source install/setup.bash
ros2 launch autoware_launch planning_simulator.launch.xml map_path:=autoware_map/sample-map-planning vehicle_model:=sample_vehicle sensor_model:=sample_sensor_kit