本文主要是介绍基于Pixhawk和ROS搭建自主无人车（五）：SLAM导航篇，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
参考
- PX4 Autopilot User Guide
- ArduPilot Documentation
- 基于Pixhawk和ROS搭建自主无人车（文章链接汇总）
1. 硬件平台
2. 环境搭建
2.1 创建工作空间
$ cd
$ mkdir -p mav_ws/src
$ cd mav_ws
$ catkin_init_workspace
ROS命令 catkin_init_workspace 分析
2.2 安装 RPLiDAR 包
$ cd ~/mav_ws/src
$ git clone https://github.com/Slamtec/rplidar_ros.git
2.3 安装 Cartographer
-
先安装一些工具
$ sudo apt-get update $ sudo apt-get install -y python-wstool python-rosdep ninja-build stow # noetic 则使用下行代码，上行默认为 melodic 版本 $ sudo apt-get install -y python3-wstool python3-rosdep ninja-build stow -
使用 wstool 重新初始化工作区，然后合并 cartographer_ros.rosinstall 文件并获取依赖项的代码
$ cd ~/mav_ws $ wstool init src $ wstool merge -t src https://raw.githubusercontent.com/googlecartographer/cartographer_ros/master/cartographer_ros.rosinstall $ wstool update -t src -
安装 proto3 和 abseil
$ wget http://fishros.com/install -O fishros && bash fishros # 根据提示选择 3 安装 rosdepc $ src/cartographer/scripts/install_proto3.sh $ sudo rosdepc init $ rosdepc update # 进行下一步之前，先注释 cartographer 包下 package.xml 中该行代码：(<depend>libabsl-dev</depend>) $ src/cartographer/scripts/install_abseil.sh $ rosdepc install --from-paths src --ignore-src --rosdistro=${ROS_DISTRO} -y -
安装 robot_pose_publisher（使用 TF 发布机器人相对于地图位置的节点）
$ cd ~/mav_ws/src $ git clone https://github.com/GT-RAIL/robot_pose_publisher.git -
创建 cartographer.launch 文件
$ cd ~/mav_ws/src/cartographer_ros/cartographer_ros/launch $ gedit cartographer.launch<!-- cartographer.launch --> <launch><param name="/use_sim_time" value="false" /><node name="cartographer_node"pkg="cartographer_ros"type="cartographer_node"args="-configuration_directory $(find cartographer_ros)/configuration_files -configuration_basename cartographer.lua"output="screen"><remap from="odom" to="/mavros/local_position/odom" /><remap from="imu" to="/mavros/imu/data" /></node><node name="cartographer_occupancy_grid_node"pkg="cartographer_ros"type="cartographer_occupancy_grid_node" /><node name="robot_pose_publisher"pkg="robot_pose_publisher"type="robot_pose_publisher"respawn="false"output="screen" ><param name="is_stamped" type="bool" value="true"/><remap from="robot_pose" to="/mavros/vision_pose/pose" /></node><node pkg="tf" type="static_transform_publisher" name="base_to_laser_broadcaster" args="0 0 0 0 0 0 base_link laser 100" /> </launch> -
创建 cartographer.lua 脚本文件
$ cd ~/mav_ws/src/cartographer_ros/cartographer_ros/configuration_files $ gedit cartographer.luainclude "map_builder.lua" include "trajectory_builder.lua"options = {map_builder = MAP_BUILDER,trajectory_builder = TRAJECTORY_BUILDER,map_frame = "map",tracking_frame = "base_link",published_frame = "base_link",odom_frame = "odom",provide_odom_frame = true,publish_frame_projected_to_2d = false,use_odometry = false,use_nav_sat = false,use_landmarks = false,num_laser_scans = 1,num_multi_echo_laser_scans = 0,num_subdivisions_per_laser_scan = 1,num_point_clouds = 0,lookup_transform_timeout_sec = 0.2,submap_publish_period_sec = 0.3,pose_publish_period_sec = 5e-3,trajectory_publish_period_sec = 30e-3,rangefinder_sampling_ratio = 1.,odometry_sampling_ratio = 1.,fixed_frame_pose_sampling_ratio = 1.,imu_sampling_ratio = 1.,landmarks_sampling_ratio = 1., }MAP_BUILDER.use_trajectory_builder_2d = trueTRAJECTORY_BUILDER_2D.min_range = 0.05 TRAJECTORY_BUILDER_2D.max_range = 30 TRAJECTORY_BUILDER_2D.missing_data_ray_length = 8.5 TRAJECTORY_BUILDER_2D.use_imu_data = false TRAJECTORY_BUILDER_2D.ceres_scan_matcher.translation_weight = 0.2 TRAJECTORY_BUILDER_2D.ceres_scan_matcher.rotation_weight = 5 TRAJECTORY_BUILDER_2D.use_online_correlative_scan_matching = true TRAJECTORY_BUILDER_2D.real_time_correlative_scan_matcher.linear_search_window = 0.1 TRAJECTORY_BUILDER_2D.real_time_correlative_scan_matcher.translation_delta_cost_weight = 1. TRAJECTORY_BUILDER_2D.real_time_correlative_scan_matcher.rotation_delta_cost_weight = 10 TRAJECTORY_BUILDER_2D.motion_filter.max_angle_radians = math.rad(0.2) -- for current lidar only 1 is good value TRAJECTORY_BUILDER_2D.num_accumulated_range_data = 1TRAJECTORY_BUILDER_2D.min_z = -0.5 TRAJECTORY_BUILDER_2D.max_z = 0.5POSE_GRAPH.constraint_builder.min_score = 0.65 POSE_GRAPH.constraint_builder.global_localization_min_score = 0.65 POSE_GRAPH.optimization_problem.huber_scale = 1e2 POSE_GRAPH.optimize_every_n_nodes = 30return options
2.4 编译工作空间
$ cd ~/mav_ws
$ catkin build
$ source devel/setup.bash
catkin_make 和catkin build这两个命令的区别
2.5 启动
-
启动 roscore
$ roscore -
启动激光雷达节点
# 本文使用 RPLiDAR A1 型号激光雷达，根据不同型号替换 rplidar_a1.launch $ roslaunch rplidar_ros rplidar_a1.launch<!-- rplidar_a1.launch --> <!-- 注意查看激光雷达连接机载电脑的端口号 /dev/ttyUSB0 --> <launch><node name="rplidarNode" pkg="rplidar_ros" type="rplidarNode" output="screen"><param name="serial_port" type="string" value="/dev/ttyUSB0"/><param name="serial_baudrate" type="int" value="115200"/><param name="frame_id" type="string" value="laser"/><param name="inverted" type="bool" value="false"/><param name="angle_compensate" type="bool" value="true"/></node> </launch> -
启动 cartographer
$ roslaunch cartographer_ros cartographer.launch -
启动 MAVROS 通信
$ roslaunch mavros apm.launch基于Pixhawk和ROS搭建自主无人车（三）：ROS通信篇
3. 配置 APM 固件参数
-
打开 Mission Planner 地面站，在全部参数表中配置以下参数（记得写入参数并重启地面站）
AHRS_EKF_TYPE = 3 EK2_ENABLE = 0 EK3_ENABLE = 1 EK3_SRC1_POSXY = 6 EK3_SRC1_POSZ = 1 EK3_SRC1_VELXY = 6 EK3_SRC1_VELZ = 6 EK3_SRC1_YAW = 6 GPS_TYPE = 0 VISO_TYPE = 1 ARMING_CHECK = 388598 -
在飞行数据界面地图上右键，然后选择 “设置家在此” >> “Set Home Here” >> “Set EKF Origin Here”，车辆应立即出现在地图上
4. 测试
- 要确认 ROS 端正常工作，请输入以下命令，并且应显示 cartographer 位置估计的实时更新
$ rostopic echo /mavros/vision_pose/pose $ rostopic info /mavros/vision_pose/pose
- 打开 Mission Planner 地面站，在飞行数据界面按 Ctrl+F，然后点击 MAVLink Inspector，出现以下界面证明 VISION_POSITION_ESTIMATE 消息已成功发送到飞控
5. 报错解决
-
FreArm：Fence requires position：将参数 FENCE_ENABLE 设置为 0
-
FreArm：servo function 33 unassigned：下述方法可行？？？
- ardupilot rover ardurover 电机相关源码 PreArm servo function 33 unassigned
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