2D pure location launch文件的配置 7
3D pure location launch文件的配置 10
Assets writer 3D launch文件的配置 12
costmap_common_params.yaml文件参数 16
local_costmap_params.yaml 文件参数 17
global_costmap_params.yaml文件参数 18
dwa_local_planner_params.yaml文件参数 18
navfn_global_planner_params.yaml文件参数 19
参数设置请参照wiki.ros.org/pointcloud_to_laserscan 21
Ros版本：ROS-Kinect
工作目录：~/carto_ws
安装依赖项
$>> sudo apt-get install libpcap-dev
下载源码到工作目录的src/文件夹下
$>> cd ~/carto_ws/src
$>> git clone https://github.com/RoboSense-LiDAR/ros_rslidar
修改文件权限
$>> cd ~/carto_ws/src/ros_rslidar/rslidar_drvier
$>> chmod 777 cfg/*
$>> cd ~/carto_ws/src/ros_rslidar/rslidar_pointcloud
$>> chmod 777 cfg/*
源码编译
$>> cd ~/carto_ws
$>> catkin_make
设置电脑的静态ip地址
$>> sudo gedit /etc/networsk/interfaces
设置eth0（有些是eth1，或者eno0等，根据自己的网口修改）,修改或增加内容如下：
# interfaces eth0 for rslidar connect
auto eth0
iface eth0 inet static
address 192.168.1.102
netmask 255.255.255.0
gateway 192.168.1.1
连接雷达电源和网线，重启电脑。
$>> cd carto_ws
$>> source devel/setup.bash
$>> roslaunch rslidar_pointcloud rs_lidar_16.launch
可以在RVIZ中看到点云,注意要设置全局坐标系为rslidar。
cartographer的安装主要是ceres、prtobuf和cartographer的安装，cartographer_ros是ROS的功能包，调用cartographer算法。
安装依赖：
$>> sudo apt-get update
$>> sudo apt-get install -y python-wstool python-rosdep ninja-build
安装Ceres solver
$>> cd ~/carto_ws/document
$>> git clone https://github.com/BlueWhaleRobot/ceres-solver.git
$>> cd ceres-solver
$>> mkdir build
$>> cd build
$>> cmake ..
$>> make -j
$>> sudo make install
安装Prtobuf 3.0
$>> cd ~/carto_ws/document
$>> git clone https://github.com/google/protobuf.git
$>> cd protobuf
$>> git checkout v3.6.1
$>> mkdir build
$>> cd build
$>> cmake \
-DCMAKE_POSITION_INDEPENDENT_CODE=ON \
-DCMAKE_BUILD_TYPE=Release \
-Dprotobuf_BUILD_TESTS=OFF \
../cmake
$>> make -j 2
$>> sudo make install
安装Cartographer
$>> cd ~/carto_ws/document
$>> git clone https://github.com/BlueWhaleRobot/cartographer.git
$>> cd cartographer
$>> mkdir build
$>> cd build
$>> cmake ..
$>> make -j
$>> sudo make install
安装cartographer_ros
$>> cd ~/carto_ws/src #请修改路径到自己的ROS catkin工作空间
$>> git clone https://github.com/BlueWhaleRobot/cartographer_ros.git
$>> cd ..
$>> catkin_make_isolated --install --use-ninja
测试
下载文件：https://www.bwbot.org/s/vQ2D9Z
然后运行。根据个人平台计算能力不同，完整运行时间一般为半个小时到1个小时之间
$>> source devel_ioslated/setup.bash
$>> roslaunch cartographer_ros offline_backpack_2d.launch bag_filenames:=${HOME}/Desktop/cartographer_paper_deutsches_museum.bag
##修改到自己保存的路径
提取建立的地图，结束测试
上面步骤会生成一个pbstream文件，用cartographer_ assets_writer可以转换成ROS使用的栅格地图。
$>> roslaunch cartographer_ros assets_writer_ros_map.launch bag_filenames:=${HOME}/Desktop/cartographer_paper_deutsche
工作目录为carto_ws，src同级目录除了编译产生的，其余都是安装依赖留下的文件夹。
Cartographer配置文件主要存在src/cartographer_ros/cartographer_ros下的urdf文件夹的myself_robot.urdf、configuration文件夹下的myself_rslidar_2(3)d.lua，myself_backpack_2(3)d_localization.lua以及rslidar_2(3)d.rviz、demo_2d.rviz配置文件、launch文件夹下的myself_rslidar_2(3)d.launch、myself_backpack_2(3)d_localization.launch。这里定位由于和后面导航rviz显示冲突，注释了rviz，纯定位时需要取消注释，显示调用rviz。
在3D建图还需要后面进行离线处理，需要assets_writer_myself_3d.launch，其中相关的配置文件为 assets_writer_myself_3d.lua。
利用bag包进行离线3D SLAM建图可以使用offline_myself_rslidar_3d.launch
URDF文件的配置（这里2D和3D都是使用的同一个urdf）：
<robot name="my_robot">
<!--设置颜色-->
<material name="orange">
<color rgba="1.0 0.5 0.2 1" />
</material>
<material name="gray">
<color rgba="0.2 0.2 0.2 1" />
</material>
<!--设置imu link，长方体，长宽高为0.06 0.04 0.02，这里的单位为米m-->
<link name="imu">
<visual>
<origin xyz="0.0 0.0 0.0" />
<geometry>
<box size="0.06 0.04 0.02" />
</geometry>
<material name="orange" />
</visual>
</link>
<!--设置lidar link，圆柱，半径0.05，高为0.07，这里的单位为米m-->
<link name="laser">
<visual>
<origin xyz="0.0 0.0 0.0" />
<geometry>
<cylinder length="0.07" radius="0.05" />
</geometry>
<material name="gray" />
</visual>
</link>
<!--设置base link，box 0.5x0.5x0.5，可根据实际机器人大小设置-->
<link name="base_link" />
<visual>
<origin xyz="0 0 0" />
<geometry>
<box size="0.5 0.5 0.5" />
</geometry>
<material name="Cyan" />
<color rgba="0 0.4 0.2 0.6"/>
</visual>
</link>
<!--设置imu和基座base的关系，imu和base没有偏移和旋转，默认为同一个坐标系，根据实际情况修改，注意子零件在父零件的坐标中的位姿表示，（右手坐标系，车头方向为x正向，车体左侧为y正向，天空为z正向）-->
<joint name="imu2base" type="fixed">
<parent link="base_link" />
<child link="imu" />
<origin xyz="0 0 0" rpy="0 0 0" />
</joint>
<!--设置lidar和基座base的关系，在base的x方向上偏移0.25，z方向偏移0.5-->
<joint name="laser2base" type="fixed">
<parent link="base_link" />
<child link="laser" />
<origin xyz="0.25 0.0 0.5" rpy="0. 0. 0." />
</joint>
</robot>
include "map_builder.lua"
include "trajectory_builder.lua"
options = {
map_builder = MAP_BUILDER,
trajectory_builder = TRAJECTORY_BUILDER,
map_frame = "map", #这个是地图坐标系名称
tracking_frame = "imu",#设置为IMU的坐标系
published_frame = "base_link",#设置为机器人基底坐标系
odom_frame = "odom",#里程计坐标系名称
provide_odom_frame = true,#发布base_link->odom的tf变化，否则发布base_link->map
publish_frame_projected_to_2d = false, #如果启用，发布的姿态将被限制为纯2D姿态(无滚动、俯仰或z-偏移)。
use_odometry = false,#是否使用编码器提供odom
use_nav_sat = false,#是否使用gps
use_landmarks = false,#是否使用路标
num_laser_scans = 0,#使用2d 雷达的数量
num_multi_echo_laser_scans = 0,#使用 echo_laser的数量
num_subdivisions_per_laser_scan = 1,
num_point_clouds = 1, #使用3D雷达的数量
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.,
}
TRAJECTORY_BUILDER_3D.num_accumulated_range_data = 1
MAP_BUILDER.use_trajectory_builder_3d = true
MAP_BUILDER.num_background_threads = 7
POSE_GRAPH.optimization_problem.huber_scale = 5e2
POSE_GRAPH.optimize_every_n_nodes = 320
POSE_GRAPH.constraint_builder.sampling_ratio = 0.03
POSE_GRAPH.optimization_problem.ceres_solver_options.max_num_iterations = 10
POSE_GRAPH.constraint_builder.min_score = 0.62
POSE_GRAPH.constraint_builder.global_localization_min_score = 0.66
return options
其他参数，参考链接：
https://google-cartographer-ros.readthedocs.io/en/latest/configuration.html
<launch>
<!--加载机器人模型-->
<param name="robot_description"
textfile="$(find cartographer_ros)/urdf/myself_robot.urdf" />
<!--发布tf-->
<node name="robot_state_publisher" pkg="robot_state_publisher"
type="robot_state_publisher" />
<!--启动建图节点-->
<node name="cartographer_node" pkg="cartographer_ros"
type="cartographer_node" args="
-configuration_directory
$(find cartographer_ros)/configuration_files
-configuration_basename myself_rslidar_2d.lua"
output="screen">
<!--重映射话题-->
<remap from="/points2" to="/rslidar_points" />
</node>
<!--启动RVIZ-->
<node name="rviz" pkg="rviz" type="rviz" required="true"
args="-d $(find cartographer_ros)/configuration_files/rslidar_2d.rviz" />
</launch>
include "myself_rslidar_2d.lua"
TRAJECTORY_BUILDER.pure_localization = true
POSE_GRAPH.optimize_every_n_nodes = 20
--fast location
MAP_BUILDER.num_background_threads=8
POSE_GRAPH.constraint_builder.sampling_ratio=0.5*POSE_GRAPH.constraint_builder.sampling_ratio
POSE_GRAPH.global_sampling_ratio=0.1*POSE_GRAPH.global_sampling_ratio
POSE_GRAPH.max_num_final_iterations=1
return options
<launch>
<param name="/use_sim_time" value="false" />
<!--加载机器人模型-->
<param name="robot_description"
textfile="$(find cartographer_ros)/urdf/myself_robot.urdf" />
<!--发布tf-->
<node name="robot_state_publisher" pkg="robot_state_publisher"
type="robot_state_publisher" />
<!--启动建图节点-->
<node name="cartographer_node" pkg="cartographer_ros"
type="cartographer_node" args="
-configuration_directory $(find cartographer_ros)/configuration_files
-configuration_basename myself_backpack_2d_localization.lua
-load_state_filename /home/dcz/bag/map.pbstream"
output="screen">
<!--重映射话题-->
<remap from="/points2" to="/rslidar_points" />
</node>
<!--需要注释下面所示文件的源码，然后重新编译-->
<!--cartographer_ros/cartographer_ros/occupancy_grid_node_main.cc //occupancy_grid_publisher_.publish(*msg_ptr); /-->
<!--发布地图-->
<node name="cartographer_occupancy_grid_node" pkg="cartographer_ros"
type="cartographer_occupancy_grid_node" args="-resolution 0.05" />
<!--启动RVIZ-->
<!--node name="rviz" pkg="rviz" type="rviz" required="true"
args="-d $(find cartographer_ros)/configuration_files/demo_2d.rviz" /-->
</launch>
include "map_builder.lua"
include "trajectory_builder.lua"
options = {
map_builder = MAP_BUILDER,
trajectory_builder = TRAJECTORY_BUILDER,
map_frame = "map",
tracking_frame = "imu",
published_frame = "base_link",
odom_frame = "odom",
provide_odom_frame = true,
publish_frame_projected_to_2d = false,
use_odometry = false,
use_nav_sat = false,
use_landmarks = false,
num_laser_scans = 0,
num_multi_echo_laser_scans = 0,
num_subdivisions_per_laser_scan = 1,
num_point_clouds = 1,
lookup_transform_timeout_sec = 0.2,
submap_publish_period_sec = 0.3,
pose_publish_period_sec = 5e-3,
trajectory_publish_period_sec = 30e-3,
rangefinder_sampling_ratio = 1.,
odometry_sampling_ratio = 1.,
fixed_frame_pose_sampling_ratio = 1.,
imu_sampling_ratio = 1.,
landmarks_sampling_ratio = 1.,
}
TRAJECTORY_BUILDER_3D.num_accumulated_range_data = 1
--change 当三维点云效果不好时使用离线建图方法，这里为离线建图需要调整的参数
TRAJECTORY_BUILDER_3D.min_range = 0.2
TRAJECTORY_BUILDER_3D.max_range = 30.
--need try
--TRAJECTORY_BUILDER_2D.min_z = 0.1
--TRAJECTORY_BUILDER_2D.max_z = 1.0
MAP_BUILDER.use_trajectory_builder_3d = true
MAP_BUILDER.num_background_threads = 7
POSE_GRAPH.optimization_problem.huber_scale = 5e2
POSE_GRAPH.optimize_every_n_nodes = 320
POSE_GRAPH.constraint_builder.sampling_ratio = 0.03
POSE_GRAPH.optimization_problem.ceres_solver_options.max_num_iterations = 10
POSE_GRAPH.constraint_builder.min_score = 0.62
POSE_GRAPH.constraint_builder.global_localization_min_score = 0.66
return options
<launch>
<!--加载机器人模型-->
<param name="robot_description"
textfile="$(find cartographer_ros)/urdf/myself_robot.urdf" />
<!--发布tf-->
<node name="robot_state_publisher" pkg="robot_state_publisher"
type="robot_state_publisher" />
<!--启动建图节点-->
<node name="cartographer_node" pkg="cartographer_ros"
type="cartographer_node" args="
-configuration_directory
$(find cartographer_ros)/configuration_files
-configuration_basename myself_rslidar_3d.lua"
output="screen">
<!--重映射话题-->
<!--remap from="/odom" to="/odom" /-->
<!--remap from="/imu" to="/imu" /-->
<remap from="/points2" to="/rslidar_points" />
</node>
<node name="cartographer_occupancy_grid_node" pkg="cartographer_ros"
type="cartographer_occupancy_grid_node" args="-resolution 0.05" />
<!--启动RVIZ-->
<node name="rviz" pkg="rviz" type="rviz" required="true"
args="-d $(find cartographer_ros)/configuration_files/demo_3d.rviz" />
include "myself_rslidar_3d.lua"
TRAJECTORY_BUILDER.pure_localization = true
POSE_GRAPH.optimize_every_n_nodes = 100
return options
<launch>
<!--加载机器人模型-->
<param name="/use_sim_time" value="false" />
<param name="robot_description"
textfile="$(find cartographer_ros)/urdf/myself_robot.urdf" />
<!--发布tf-->
<node name="robot_state_publisher" pkg="robot_state_publisher"
type="robot_state_publisher" />
<!--启动定位节点-->
<node name="cartographer_node" pkg="cartographer_ros"
type="cartographer_node" args="
-configuration_directory $(find cartographer_ros)/configuration_files
-configuration_basename myself_backpack_3d_localization.lua
-load_state_filename $(arg load_state_filename)"
output="screen">
<remap from="/points2" to="/rslidar_points" />
<!--remap from="/imu" to="/your_imu_topic" /-->
</node>
<node name="cartographer_occupancy_grid_node" pkg="cartographer_ros"
type="cartographer_occupancy_grid_node" args="-resolution 0.05" />
<!--启动RVIZ-->
<node name="rviz" pkg="rviz" type="rviz" required="true"
args="-d $(find cartographer_ros)/configuration_files/demo_3d.rviz" />
</launch>
VOXEL_SIZE = 5e-2
include "transform.lua"
options = {
tracking_frame = "imu", #imu坐标系
pipeline = {
{
action = "min_max_range_filter",
min_range = 1.,
max_range = 20.,
},
{
action = "dump_num_points",
},
{
action = "fixed_ratio_sampler",
sampling_ratio = 0.01,
},
{
action = "write_ply",
filename = "points.ply"
},
-- Gray X-Rays. These only use geometry to color pixels.
-- Now we recolor our points by frame and write another batch of X-Rays. It
-- is visible in them what was seen by the horizontal and the vertical
-- laser.
{
action = "color_points",
frame_id = "rslidar",#雷达坐标系
color = { 255., 0., 0. },
},
{
action = "write_xray_image",
voxel_size = VOXEL_SIZE,
filename = "xray_xy_all_color",
transform = XY_TRANSFORM,
},
}
}
return options
<launch>
<node name="cartographer_assets_writer" pkg="cartographer_ros" required="true"
type="cartographer_assets_writer" args="
-configuration_directory $(find cartographer_ros)/configuration_files
-configuration_basename assets_writer_myself_3d.lua
-urdf_filename $(find cartographer_ros)/urdf/myself_robot.urdf
-bag_filenames $(arg bag_filenames)
-pose_graph_filename $(arg pose_graph_filename)"
output="screen">
</node>
</launch>
#SLAM
启动雷达点云
$>> roslaunch rslidar_pointcloud cloud_nodelet.launch
启动Cartographer建图
$>> roslaunch cartographer_ros myself_rslidar_2d.launch
Or
$>> roslaunch cartographer_ros myself_rslidar_3d.launch
Tips：纯定位需要修改源码，所以如果建图前请确保，src/cartographer_ros/cartographer_ros/cartographer_ros/occupancy_grid_node.main.cc中main函数上面第五行是否注释了
occupancy_grid_publisher_.publish(*msg);
纯定位注释掉，建图时打开，然后执行catkin_make，再执行建图或者定位。
3d建图需要先发布imu ，并且需要rosbag保存点云和imu消息：
$>> rosrun imu_pub imu_pub_node
$>> rosbag record /imu /rslidar_points
结束建图
$>> rosservice call /finish_trajectory 0
保存地图，在~/.ros目录下
$>> rosservice call /write_state “filenames: ’map.pbstream’”
拷贝出地图转化地图到ros使用的栅格地图yaml文件，使用绝对路径
$>> rosrun cartographer_ros cartographer_pbstream_to_ros_map -bpstream_filename path_of_pbstream/map.pbstream
3d点云地图转化与查看
地图格式Bag转ply
$>> roslaunch cartographer_ros assert_writer_myself_3d.launch bag_filenames:=~/bag_path/2020.....bag.bag pose_graph_filename:=~/pbstream_path/map.pbstream
地图格式Ply转pcd
$>> pcl_ply2pcd your.bag_points.ply your_pcd_filename.pcd
查看点云
$>> pcl_viewer your_pcd_filename.pcd
如果效果不好，使用offline_myself_rslidar_3d.launch重新建图，记得更改launch加载的bag文件路径，如果不满意，修改myself_rslidar_3d.lua文件的TRAJECTORY_BUILD_3D.max_range的大小，建议区间在30-80，之至点云图满意收敛。
命令：
$>> roslaunch cartographe_ros myself_backpack_2(3)d_localization.launch
根据实际情况加载load_state_filename，如果未显示rviz，请查看launch文件是否注释了rviz的显示。
安装导航功能包
$>> sudo apt-get install ros-kinect-navigation
导航包主要是launch文件加cost_map、planner参数配置文件。
<launch>
<!-- map server 提供地图-->
<arg name="map" default="/home/dcz/bag/map.yaml" />
<node name="map_server" pkg="map_server" type="map_server" args="$(arg map)"/>
<!-- map server 重映射话题名和坐标系-->
<arg name="odom_frame_id" default="odom"/>
<arg name="base_frame_id" default="base_link"/>
<arg name="global_frame_id" default="map"/>
<arg name="odom_topic" default="odom" />
<arg name="laser_topic" default="scan" />
<!-- 静态坐标-->
<node pkg="tf" type="static_transform_publisher" name="map_odom_broadcaster" args="0 0 0 0 0 0 /map /odom 100" />
<!-- 加载cost_map 和 planner，分为local 和 global-->
<node pkg="move_base" type="move_base" respawn="false" name="move_base" output="screen">
<rosparam file="$(find navigation)/config/param/costmap_common_params.yaml" command="load" ns="global_costmap" />
<rosparam file="$(find navigation)/config/param/costmap_common_params.yaml" command="load" ns="local_costmap" />
<rosparam file="$(find navigation)/config/param/local_costmap_params.yaml" command="load" />
<rosparam file="$(find navigation)/config/param/global_costmap_params.yaml" command="load" />
<rosparam file="$(find navigation)/config/param/dwa_local_planner_params.yaml" command="load" />
<rosparam file="$(find navigation)/config/param/move_base_params.yaml" command="load" />
<rosparam file="$(find navigation)/config/param/global_planner_params.yaml" command="load" />
<rosparam file="$(find navigation)/config/param/navfn_global_planner_params.yaml" command="load" />
<!-- reset frame_id parameters using user input data -->
<param name="global_costmap/global_frame" value="$(arg global_frame_id)"/>
<param name="global_costmap/robot_base_frame" value="$(arg base_frame_id)"/>
<param name="local_costmap/global_frame" value="$(arg odom_frame_id)"/>
<param name="local_costmap/robot_base_frame" value="$(arg base_frame_id)"/>
<param name="DWAPlannerROS/global_frame_id" value="$(arg odom_frame_id)"/>
<remap from="odom" to="$(arg odom_topic)"/>
<remap from="scan" to="$(arg laser_topic)"/>
<!--remap from="cmd_vel" to="/cmd_vel_mux/input/navi"/-->
</node>
<!--调用rviz -->
<!--node pkg="rviz" type="rviz" name="rviz" args="-d $(find navigation)/rviz/nav.rviz"/-->
</launch>
# Move base node parameters. For full documentation of the parameters in this file, please see
#
# http://www.ros.org/wiki/move_base
#
#启用costmap
shutdown_costmaps: false
#设置控制频率
controller_frequency: 5.0
controller_patience: 3.0
planner_frequency: 1.0
planner_patience: 5.0
oscillation_timeout: 10.0
oscillation_distance: 0.2
#选择使用dwa local planner和navfn global planner
# local planner - default is trajectory rollout
base_local_planner: "dwa_local_planner/DWAPlannerROS"
base_global_planner: "navfn/NavfnROS" #alternatives: global_planner/GlobalPlanner, carrot_planner/CarrotPlanner
#这里定义恢复行为，当机器人震荡时，会按顺序进行恢复行为，清除障碍物，这里注释了使用默认的恢复行为。
#We plan to integrate recovery behaviors for turtlebot but currently those belong to gopher and still have to be adapted.
## recovery behaviors; we avoid spinning, but we need a fall-back replanning
#recovery_behavior_enabled: true
#recovery_behaviors:
#- name: 'super_conservative_reset1'
#type: 'clear_costmap_recovery/ClearCostmapRecovery'
#- name: 'conservative_reset1'
#type: 'clear_costmap_recovery/ClearCostmapRecovery'
#- name: 'aggressive_reset1'
#type: 'clear_costmap_recovery/ClearCostmapRecovery'
#- name: 'clearing_rotation1'
#type: 'rotate_recovery/RotateRecovery'
#- name: 'super_conservative_reset2'
#type: 'clear_costmap_recovery/ClearCostmapRecovery'
#- name: 'conservative_reset2'
#type: 'clear_costmap_recovery/ClearCostmapRecovery'
#- name: 'aggressive_reset2'
#type: 'clear_costmap_recovery/ClearCostmapRecovery'
#- name: 'clearing_rotation2'
#type: 'rotate_recovery/RotateRecovery'
#super_conservative_reset1:
#reset_distance: 3.0
#conservative_reset1:
#reset_distance: 1.5
#aggressive_reset1:
#reset_distance: 0.0
#super_conservative_reset2:
#reset_distance: 3.0
#conservative_reset2:
#reset_distance: 1.5
#aggressive_reset2:
#reset_distance: 0.0
costmap_common_params.yaml文件参数
max_obstacle_height: 1.40 # assume something like an arm is mounted on top of the robot
# Obstacle Cost Shaping (http://wiki.ros.org/costmap_2d/hydro/inflation)
#设置机器人半径或者底盘轮廓四个点
robot_radius: 0.25 # distance a circular robot should be clear of the obstacle (kobuki: 0.18)
# footprint: [[x0, y0], [x1, y1], ... [xn, yn]] # if the robot is not circular
map_type: voxel
voxel_layer:
enabled: true
max_obstacle_height: 2.2
origin_z: 0.0
z_resolution: 0.1
z_voxels: 22
unknown_threshold: 15
mark_threshold: 0
combination_method: 1
track_unknown_space: true #true needed for disabling global path planning through unknown space
obstacle_range: 2.5
raytrace_range: 3.0
publish_voxel_map: true
observation_sources: bump #camera scan bump
#设置检测障碍物的信息源
# scan:
# data_type: LaserScan
# topic: scan
# marking: true
# clearing: true
# min_obstacle_height: 0.1
# max_obstacle_height: 0.3
# camera:
# data_type: PointCloud2
# topic: camera/depth/points
# marking: true
# clearing: true
# min_obstacle_height: 0.0
# max_obstacle_height: 2.0
bump:
data_type: PointCloud2 #信息源的类型
topic: /rslidar_points #信息源话题
marking: true
clearing: true
min_obstacle_height: 0.0
max_obstacle_height: 0.15
#cost_scaling_factor and inflation_radius were now moved to the inflation_layer ns
inflation_layer:
enabled: true
cost_scaling_factor: 2.58 # exponential rate at which the obstacle cost drops off (default: 10)
#设置障碍物膨胀半径
inflation_radius: 0.5 # max. distance from an obstacle at which costs are incurred for planning paths.
static_layer:
enabled: true
local_costmap_params.yaml 文件参数
local_costmap:
global_frame: odom #/map
robot_base_frame: base_link
update_frequency: 3.0 #5.0
publish_frequency: 2.0
static_map: false #false
#局部cost_map采用滑动窗口，进行膨胀
rolling_window: true
width: 4.0
height: 4.0
resolution: 0.05
origin_x: 5.0
origin_y: 0
transform_tolerance: 0.5
plugins:
- {name: voxel_layer, type: "costmap_2d::VoxelLayer"}
- {name: inflation_layer, type: "costmap_2d::InflationLayer"}
global_costmap_params.yaml文件参数
global_costmap:
global_frame: map
robot_base_frame: base_link
update_frequency: 2.0
publish_frequency: 0.5
static_map: true
rolling_window: false
transform_tolerance: 0.5
plugins:
- {name: static_layer, type: "costmap_2d::StaticLayer"}
- {name: voxel_layer, type: "costmap_2d::VoxelLayer"}
- {name: inflation_layer, type: "costmap_2d::InflationLayer"}
dwa_local_planner_params.yaml文件参数
DWAPlannerROS:
#设置机器人的速度和旋转速度
# Robot Configuration Parameters
max_vel_x: 0.5
min_vel_x: 0.0
max_vel_y: 0.0
min_vel_y: 0.0
# The velocity when robot is moving in a straight line
max_trans_vel: 0.55
min_trans_vel: 0.1
trans_stopped_vel: 0.1
max_rot_vel: 5.0
min_rot_vel: 0.8
rot_stopped_vel: 0.8
acc_lim_x: 1.0
acc_lim_theta: 2.0
acc_lim_y: 0.0
#目标点误差，如果过小会导致机器人在目标点震荡 单位为米和弧度
# Goal Tolerance Parametes
yaw_goal_tolerance: 0.3
xy_goal_tolerance: 0.15
# latch_xy_goal_tolerance: false
# Forward Simulation Parameters
sim_time: 4.0
vx_samples: 20
vy_samples: 0
vtheta_samples: 40
# Trajectory Scoring Parameters
path_distance_bias: 32.0
goal_distance_bias: 24.0
occdist_scale: 0.1
forward_point_distance: 0.325
stop_time_buffer: 0.2
scaling_speed: 0.25
max_scaling_factor: 0.2
# Oscillation Prevention Parameters
oscillation_reset_dist: 0.05
# Debugging
publish_traj_pc : true
publish_cost_grid_pc: true
global_frame_id: odom
navfn_global_planner_params.yaml文件参数
NavfnROS:
visualize_potential: false #Publish potential for rviz as pointcloud2, not really helpful, default false
allow_unknown: true #Specifies whether or not to allow navfn to create plans that traverse unknown space, default true
#Needs to have track_unknown_space: true in the obstacle / voxel layer (in costmap_commons_param) to work
planner_window_x: 0.0 #Specifies the x size of an optional window to restrict the planner to, default 0.0
planner_window_y: 0.0 #Specifies the y size of an optional window to restrict the planner to, default 0.0
default_tolerance: 0.0 #If the goal is in an obstacle, the planer will plan to the nearest point in the radius of default_tolerance, default 0.0
#The area is always searched, so could be slow for big values
采用Cartographer定位和navegation stack中的move_base导航。
命令：
$>> roslaunch rslidar_pointcloud cloud_nodelet.launch
$>> roslaunch cartographe_ros myself_backpack_2d_localization.launch
$>> roslaunch navigation zky_move_base.launch
$>> rosrun cmd_to_stm cmd_to_stm
然后使用RVIZ中的2D navigation goal 选择图中的一点，可以在终端 rostopic echo /simple/goal 查看。选中的点是相对机器人坐标，非全局坐标。下面的设置自定义导航点时需要注意。
规定好路径点和达到目标点时的位姿，输入在源代码的数组中，前三位表示xyz的平移，后四位表示到达目标点时的位姿，0 0 0 1，表示不改变方向，0 0 1 0，表示180°方向，0 0 0.707 0.707 表示90°，0 0 -0.707 0.707表示270°，逆时针方向为正。
规划好目标点，程序依次喂入目标点，实现路线规划导航。
在导航命令基础上运行，命令：
$>> rosrun simple_navigation_goal simple_navigation_goal
启动雷达和建图
$>> roslaunch rslidar_pointcloud cloud_nodelet.launch
启动建图，
（Cartographer存在问题，如何发步ros格式的map）暂时以hector SLAM作为建图
$>> roslaunch pointcloud_to_laserscan point_to_scan.launch
$>> roslaunch navigation hector.launch
启动导航
$>> roslaunch navigation zky_move_base.launch
$>> rosrun cmd_to_stm cmd_to_stm
启动目标点(更改目标点列表元素，适应场景)
$>> rosrun navigation explor_slam.py
根据imu通讯协议，读取imu的xyz加速度和四元数，发布/imu消息。
根据编码器数据或cmd_vel发布/odom
$>> rosrun cmd_vel_to_stm cmd_to_stm
程序通过监听/cmd_vel话题，将速度信息发送给下位机
$>> rosrun cmd_vel_to_stm robot_teleop.py
程序通过键盘发布cmd_vel,配合上面的对机器人进行键盘遥控。
参数设置请参照wiki.ros.org/pointcloud_to_laserscan
$>> roslaunch pointcloud_to_laserscan point_to_scan.launch
其中重映射了cloud_in->rslidar_points
发布/scan话题
ROS移动机器人基于RRT（快速探索随机树）算法 rrt_exploration实现真实机器人自主探索建图： https://blog.csdn.net/qq_42145185/article/details/82461072
PIBOT导航机器人： https://www.jianshu.com/u/7f508db63608