eutobot dependency node skip
parent
9d916a95d3
commit
c7604fcfa6
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SHELL=/bin/bash
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SESSION_MANAGER=local/hjmatt-linux:@/tmp/.ICE-unix/2257,unix/hjmatt-linux:/tmp/.ICE-unix/2257
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QT_ACCESSIBILITY=1
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COLORTERM=truecolor
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XDG_CONFIG_DIRS=/etc/xdg/xdg-ubuntu:/etc/xdg
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XDG_MENU_PREFIX=gnome-
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GNOME_DESKTOP_SESSION_ID=this-is-deprecated
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TERMINATOR_DBUS_PATH=/net/tenshu/Terminator2
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LC_ADDRESS=ko_KR.UTF-8
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GNOME_SHELL_SESSION_MODE=ubuntu
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LC_NAME=ko_KR.UTF-8
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SSH_AUTH_SOCK=/run/user/1000/keyring/ssh
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ELECTRON_RUN_AS_NODE=1
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TERMINATOR_UUID=urn:uuid:1b1c9186-aec3-4494-ab5a-f51a9555abf6
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XMODIFIERS=@im=ibus
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DESKTOP_SESSION=ubuntu
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LC_MONETARY=ko_KR.UTF-8
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SSH_AGENT_PID=2219
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VSCODE_AMD_ENTRYPOINT=vs/workbench/api/node/extensionHostProcess
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GTK_MODULES=gail:atk-bridge
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PWD=/home/hjmatt/task8/13.ros/SRDK/mnt_target/workspace/src
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XDG_SESSION_DESKTOP=ubuntu
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LOGNAME=hjmatt
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XDG_SESSION_TYPE=x11
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GPG_AGENT_INFO=/run/user/1000/gnupg/S.gpg-agent:0:1
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VSCODE_CODE_CACHE_PATH=/home/hjmatt/.config/Code/CachedData/1a5daa3a0231a0fbba4f14db7ec463cf99d7768e
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_=/usr/bin/env
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XAUTHORITY=/run/user/1000/gdm/Xauthority
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GJS_DEBUG_TOPICS=JS ERROR;JS LOG
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WINDOWPATH=2
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HOME=/home/hjmatt
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USERNAME=hjmatt
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IM_CONFIG_PHASE=1
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LC_PAPER=ko_KR.UTF-8
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LANG=en_US.UTF-8
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LS_COLORS=rs=0:di=01;34:ln=01;36:mh=00:pi=40;33:so=01;35:do=01;35:bd=40;33;01:cd=40;33;01:or=40;31;01:mi=00:su=37;41:sg=30;43:ca=30;41:tw=30;42:ow=34;42:st=37;44:ex=01;32:*.tar=01;31:*.tgz=01;31:*.arc=01;31:*.arj=01;31:*.taz=01;31:*.lha=01;31:*.lz4=01;31:*.lzh=01;31:*.lzma=01;31:*.tlz=01;31:*.txz=01;31:*.tzo=01;31:*.t7z=01;31:*.zip=01;31:*.z=01;31:*.dz=01;31:*.gz=01;31:*.lrz=01;31:*.lz=01;31:*.lzo=01;31:*.xz=01;31:*.zst=01;31:*.tzst=01;31:*.bz2=01;31:*.bz=01;31:*.tbz=01;31:*.tbz2=01;31:*.tz=01;31:*.deb=01;31:*.rpm=01;31:*.jar=01;31:*.war=01;31:*.ear=01;31:*.sar=01;31:*.rar=01;31:*.alz=01;31:*.ace=01;31:*.zoo=01;31:*.cpio=01;31:*.7z=01;31:*.rz=01;31:*.cab=01;31:*.wim=01;31:*.swm=01;31:*.dwm=01;31:*.esd=01;31:*.jpg=01;35:*.jpeg=01;35:*.mjpg=01;35:*.mjpeg=01;35:*.gif=01;35:*.bmp=01;35:*.pbm=01;35:*.pgm=01;35:*.ppm=01;35:*.tga=01;35:*.xbm=01;35:*.xpm=01;35:*.tif=01;35:*.tiff=01;35:*.png=01;35:*.svg=01;35:*.svgz=01;35:*.mng=01;35:*.pcx=01;35:*.mov=01;35:*.mpg=01;35:*.mpeg=01;35:*.m2v=01;35:*.mkv=01;35:*.webm=01;35:*.ogm=01;35:*.mp4=01;35:*.m4v=01;35:*.mp4v=01;35:*.vob=01;35:*.qt=01;35:*.nuv=01;35:*.wmv=01;35:*.asf=01;35:*.rm=01;35:*.rmvb=01;35:*.flc=01;35:*.avi=01;35:*.fli=01;35:*.flv=01;35:*.gl=01;35:*.dl=01;35:*.xcf=01;35:*.xwd=01;35:*.yuv=01;35:*.cgm=01;35:*.emf=01;35:*.ogv=01;35:*.ogx=01;35:*.aac=00;36:*.au=00;36:*.flac=00;36:*.m4a=00;36:*.mid=00;36:*.midi=00;36:*.mka=00;36:*.mp3=00;36:*.mpc=00;36:*.ogg=00;36:*.ra=00;36:*.wav=00;36:*.oga=00;36:*.opus=00;36:*.spx=00;36:*.xspf=00;36:
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XDG_CURRENT_DESKTOP=Unity
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VSCODE_IPC_HOOK=/run/user/1000/vscode-7e50df75-1.84-main.sock
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VTE_VERSION=6003
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VSCODE_CLI=1
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INVOCATION_ID=67cab6c134a244dba56513793b4cab58
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TERMINATOR_DBUS_NAME=net.tenshu.Terminator21a9d5db22c73a993ff0b42f64b396873
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MANAGERPID=2041
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CHROME_DESKTOP=code-url-handler.desktop
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GJS_DEBUG_OUTPUT=stderr
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LESSCLOSE=/usr/bin/lesspipe %s %s
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XDG_SESSION_CLASS=user
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TERM=xterm-256color
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LC_IDENTIFICATION=ko_KR.UTF-8
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LESSOPEN=| /usr/bin/lesspipe %s
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USER=hjmatt
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DISPLAY=:0
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VSCODE_PID=609928
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SHLVL=1
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LC_TELEPHONE=ko_KR.UTF-8
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QT_IM_MODULE=ibus
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LC_MEASUREMENT=ko_KR.UTF-8
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VSCODE_CWD=/home/hjmatt/task8/13.ros/SRDK/mnt_target/workspace/src
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VSCODE_CRASH_REPORTER_PROCESS_TYPE=extensionHost
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XDG_RUNTIME_DIR=/run/user/1000
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LC_TIME=ko_KR.UTF-8
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ELECTRON_NO_ATTACH_CONSOLE=1
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JOURNAL_STREAM=8:55273
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XDG_DATA_DIRS=/usr/share/ubuntu:/usr/local/share/:/usr/share/
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GDK_BACKEND=x11
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PATH=/home/hjmatt/.local/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin
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GDMSESSION=ubuntu
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ORIGINAL_XDG_CURRENT_DESKTOP=ubuntu:GNOME
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DBUS_SESSION_BUS_ADDRESS=unix:path=/run/user/1000/bus
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VSCODE_NLS_CONFIG={"locale":"en-us","osLocale":"en-us","availableLanguages":{},"_languagePackSupport":true}
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GIO_LAUNCHED_DESKTOP_FILE_PID=2710
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GIO_LAUNCHED_DESKTOP_FILE=/usr/share/applications/terminator.desktop
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VSCODE_HANDLES_UNCAUGHT_ERRORS=true
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LC_NUMERIC=ko_KR.UTF-8
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OLDPWD=/home/hjmatt/task8/13.ros/SRDK/mnt_target/workspace
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@ -32,41 +32,23 @@ from launch_ros.parameter_descriptions import ParameterValue
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def generate_launch_description():
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def generate_launch_description():
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LDS_MODEL = os.environ['LDS_MODEL']
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LDS_LAUNCH_FILE = '/hlds_laser.launch.py'
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# Dynamixel USB Port
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# Dynamixel USB Port
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dynamixel_usb_port = LaunchConfiguration('dynamixel_usb_port', default='/dev/ttyUSB0')
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dynamixel_usb_port = LaunchConfiguration('dynamixel_usb_port', default='/dev/ttyUSB1')
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eutobot_param_dir = LaunchConfiguration(
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# eutobot_param_dir = LaunchConfiguration(
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'eutobot_param_dir',
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# 'eutobot_param_dir',
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default=os.path.join(
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# default=os.path.join(
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get_package_share_directory('eutobot_bringup'),
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# get_package_share_directory('eutobot_bringup'),
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'param',
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# 'param',
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'eutobot.yaml'))
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# 'eutobot.yaml'))
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if LDS_MODEL == 'LDS-01':
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lidar_pkg_dir = LaunchConfiguration(
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'lidar_pkg_dir',
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default=os.path.join(get_package_share_directory('hls_lfcd_lds_driver'), 'launch'))
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elif LDS_MODEL == 'LDS-02':
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lidar_pkg_dir = LaunchConfiguration(
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'lidar_pkg_dir',
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default=os.path.join(get_package_share_directory('ld08_driver'), 'launch'))
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LDS_LAUNCH_FILE = '/ld08.launch.py'
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elif LDS_MODEL == 'YDLIDAR':
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lidar_pkg_dir = LaunchConfiguration(
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lidar_pkg_dir = LaunchConfiguration(
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'lidar_pkg_dir',
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'lidar_pkg_dir',
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default=os.path.join(get_package_share_directory('ydlidar_ros2_driver'), 'launch'))
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default=os.path.join(get_package_share_directory('ydlidar_ros2_driver'), 'launch'))
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LDS_LAUNCH_FILE = '/ydlidar_launch.py'
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# LDS_LAUNCH_FILE = '/ydlidar_launch.py'
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else:
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lidar_pkg_dir = LaunchConfiguration(
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'lidar_pkg_dir',
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default=os.path.join(get_package_share_directory('hls_lfcd_lds_driver'), 'launch'))
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use_sim_time = LaunchConfiguration('use_sim_time', default='false')
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use_sim_time = LaunchConfiguration('use_sim_time', default='false')
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# YAHBOOM
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# YAHBOOM
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yahboom_pkg_dir = LaunchConfiguration(
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yahboom_pkg_dir = LaunchConfiguration(
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'yahboom_pkg_dir',
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'yahboom_pkg_dir',
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@ -79,19 +61,19 @@ def generate_launch_description():
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default_value=use_sim_time,
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default_value=use_sim_time,
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description='Use simulation (Gazebo) clock if true'),
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description='Use simulation (Gazebo) clock if true'),
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DeclareLaunchArgument(
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# DeclareLaunchArgument(
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'eutobot_param_dir',
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# 'eutobot_param_dir',
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default_value=eutobot_param_dir,
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# default_value=eutobot_param_dir,
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description='Full path to eutobot parameter file to load'),
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# description='Full path to eutobot parameter file to load'),
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# IncludeLaunchDescription(
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# PythonLaunchDescriptionSource(
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# [ThisLaunchFileDir(), '/eutobot_state_publisher.launch.py']),
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# launch_arguments={'use_sim_time': use_sim_time}.items(),
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# ),
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IncludeLaunchDescription(
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IncludeLaunchDescription(
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PythonLaunchDescriptionSource(
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PythonLaunchDescriptionSource([lidar_pkg_dir, '/ydlidar_launch.py']),
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[ThisLaunchFileDir(), '/eutobot_state_publisher.launch.py']),
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launch_arguments={'use_sim_time': use_sim_time}.items(),
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),
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IncludeLaunchDescription(
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PythonLaunchDescriptionSource([lidar_pkg_dir, LDS_LAUNCH_FILE]),
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launch_arguments={'port': '/dev/ttySAC6', 'frame_id': 'base_scan'}.items(),
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launch_arguments={'port': '/dev/ttySAC6', 'frame_id': 'base_scan'}.items(),
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),
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),
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@ -99,10 +81,10 @@ def generate_launch_description():
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PythonLaunchDescriptionSource([yahboom_pkg_dir, "/yahboomcar_bringup_X3_launch.py"]),
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PythonLaunchDescriptionSource([yahboom_pkg_dir, "/yahboomcar_bringup_X3_launch.py"]),
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),
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),
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Node(
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# Node(
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package='eutobot_node',
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# package='eutobot_node',
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executable='eutobot_ros',
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# executable='eutobot_ros',
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parameters=[eutobot_param_dir],
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# parameters=[eutobot_param_dir],
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arguments=['-i', dynamixel_usb_port],
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# arguments=['-i', dynamixel_usb_port],
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output='screen'),
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# output='screen'),
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])
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])
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@ -65,6 +65,12 @@ def generate_launch_description():
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'imu_filter_param.yaml'
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'imu_filter_param.yaml'
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)
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)
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ekf_filter_config = os.path.join(
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get_package_share_directory('yahboomcar_bringup'),
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'param',
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'ekf.yaml'
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)
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driver_node = Node(
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driver_node = Node(
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package='yahboomcar_bringup',
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package='yahboomcar_bringup',
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executable='Mcnamu_driver_X3',
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executable='Mcnamu_driver_X3',
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@ -88,13 +94,11 @@ def generate_launch_description():
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executable='ekf_node',
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executable='ekf_node',
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name='ekf_filter_node',
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name='ekf_filter_node',
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output='screen',
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output='screen',
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parameters=[
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parameters=[ekf_filter_config],
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os.path.join(get_package_share_directory("robot_localization"), 'params', 'ekf.yaml')
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],
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remappings=[
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remappings=[
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("/example/imu", "/imu/data"),
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('/odom0', '/odom_raw'),
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("/example/odom", "/odom_raw"),
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('/imu0', '/imu/data'),
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("/odometry/filtered", "/odom"),
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('/odometry/filtered', '/odom')
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]
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]
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)
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)
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### ekf config file ###
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ekf_filter_node:
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ros__parameters:
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# The frequency, in Hz, at which the filter will output a position estimate. Note that the filter will not begin
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# computation until it receives at least one message from one of the inputs. It will then run continuously at the
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# frequency specified here, regardless of whether it receives more measurements. Defaults to 30 if unspecified.
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frequency: 20.0
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# The period, in seconds, after which we consider a sensor to have timed out. In this event, we carry out a predict
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# cycle on the EKF without correcting it. This parameter can be thought of as the minimum frequency with which the
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# filter will generate new output. Defaults to 1 / frequency if not specified.
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sensor_timeout: 0.1
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# ekf_localization_node and ukf_localization_node both use a 3D omnidirectional motion model. If this parameter is
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# set to true, no 3D information will be used in your state estimate. Use this if you are operating in a planar
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# environment and want to ignore the effect of small variations in the ground plane that might otherwise be detected
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# by, for example, an IMU. Defaults to false if unspecified.
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two_d_mode: true
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# Use this parameter to provide an offset to the transform generated by ekf_localization_node. This can be used for
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# future dating the transform, which is required for interaction with some other packages. Defaults to 0.0 if
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# unspecified.
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transform_time_offset: 0.0
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# Use this parameter to provide specify how long the tf listener should wait for a transform to become available.
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# Defaults to 0.0 if unspecified.
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transform_timeout: 0.0
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# If you're having trouble, try setting this to true, and then echo the /diagnostics_agg topic to see if the node is
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# unhappy with any settings or data.
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print_diagnostics: true
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# Debug settings. Not for the faint of heart. Outputs a ludicrous amount of information to the file specified by
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# debug_out_file. I hope you like matrices! Please note that setting this to true will have strongly deleterious
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# effects on the performance of the node. Defaults to false if unspecified.
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debug: false
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# Defaults to "robot_localization_debug.txt" if unspecified. Please specify the full path.
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debug_out_file: /path/to/debug/file.txt
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# Whether we'll allow old measurements to cause a re-publication of the updated state
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permit_corrected_publication: false
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# Whether to publish the acceleration state. Defaults to false if unspecified.
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publish_acceleration: false
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# Whether to broadcast the transformation over the /tf topic. Defaults to true if unspecified.
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#publish_tf: true
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# REP-105 (http://www.ros.org/reps/rep-0105.html) specifies four principal coordinate frames: base_link, odom, map, and
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# earth. base_link is the coordinate frame that is affixed to the robot. Both odom and map are world-fixed frames.
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# The robot's position in the odom frame will drift over time, but is accurate in the short term and should be
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# continuous. The odom frame is therefore the best frame for executing local motion plans. The map frame, like the odom
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# frame, is a world-fixed coordinate frame, and while it contains the most globally accurate position estimate for your
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# robot, it is subject to discrete jumps, e.g., due to the fusion of GPS data or a correction from a map-based
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# localization node. The earth frame is used to relate multiple map frames by giving them a common reference frame.
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# ekf_localization_node and ukf_localization_node are not concerned with the earth frame.
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# Here is how to use the following settings:
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# 1. Set the map_frame, odom_frame, and base_link frames to the appropriate frame names for your system.
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# 1a. If your system does not have a map_frame, just remove it, and make sure "world_frame" is set to the value of
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# odom_frame.
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# 2. If you are fusing continuous position data such as wheel encoder odometry, visual odometry, or IMU data, set
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# "world_frame" to your odom_frame value. This is the default behavior for robot_localization's state estimation nodes.
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# 3. If you are fusing global absolute position data that is subject to discrete jumps (e.g., GPS or position updates
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# from landmark observations) then:
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# 3a. Set your "world_frame" to your map_frame value
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# 3b. MAKE SURE something else is generating the odom->base_link transform. Note that this can even be another state
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# estimation node from robot_localization! However, that instance should *not* fuse the global data.
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map_frame: map # Defaults to "map" if unspecified
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||||||
|
odom_frame: odom # Defaults to "odom" if unspecified
|
||||||
|
base_link_frame: base_footprint # Defaults to "base_link" if unspecified
|
||||||
|
world_frame: odom # Defaults to the value of odom_frame if unspecified
|
||||||
|
|
||||||
|
# The filter accepts an arbitrary number of inputs from each input message type (nav_msgs/Odometry,
|
||||||
|
# geometry_msgs/PoseWithCovarianceStamped, geometry_msgs/TwistWithCovarianceStamped,
|
||||||
|
# sensor_msgs/Imu). To add an input, simply append the next number in the sequence to its "base" name, e.g., odom0,
|
||||||
|
# odom1, twist0, twist1, imu0, imu1, imu2, etc. The value should be the topic name. These parameters obviously have no
|
||||||
|
# default values, and must be specified.
|
||||||
|
odom0: /odom0
|
||||||
|
|
||||||
|
# Each sensor reading updates some or all of the filter's state. These options give you greater control over which
|
||||||
|
# values from each measurement are fed to the filter. For example, if you have an odometry message as input, but only
|
||||||
|
# want to use its Z position value, then set the entire vector to false, except for the third entry. The order of the
|
||||||
|
# values is x, y, z, roll, pitch, yaw, vx, vy, vz, vroll, vpitch, vyaw, ax, ay, az. Note that not some message types
|
||||||
|
# do not provide some of the state variables estimated by the filter. For example, a TwistWithCovarianceStamped message
|
||||||
|
# has no pose information, so the first six values would be meaningless in that case. Each vector defaults to all false
|
||||||
|
# if unspecified, effectively making this parameter required for each sensor.
|
||||||
|
odom0_config: [false, false, false,
|
||||||
|
false, false, false,
|
||||||
|
true, true, false,
|
||||||
|
false, false, true,
|
||||||
|
false, false, false]
|
||||||
|
|
||||||
|
# If you have high-frequency data or are running with a low frequency parameter value, then you may want to increase
|
||||||
|
# the size of the subscription queue so that more measurements are fused.
|
||||||
|
odom0_queue_size: 1
|
||||||
|
|
||||||
|
# [ADVANCED] Large messages in ROS can exhibit strange behavior when they arrive at a high frequency. This is a result
|
||||||
|
# of Nagle's algorithm. This option tells the ROS subscriber to use the tcpNoDelay option, which disables Nagle's
|
||||||
|
# algorithm.
|
||||||
|
odom0_nodelay: false
|
||||||
|
|
||||||
|
# [ADVANCED] When measuring one pose variable with two sensors, a situation can arise in which both sensors under-
|
||||||
|
# report their covariances. This can lead to the filter rapidly jumping back and forth between each measurement as they
|
||||||
|
# arrive. In these cases, it often makes sense to (a) correct the measurement covariances, or (b) if velocity is also
|
||||||
|
# measured by one of the sensors, let one sensor measure pose, and the other velocity. However, doing (a) or (b) isn't
|
||||||
|
# always feasible, and so we expose the differential parameter. When differential mode is enabled, all absolute pose
|
||||||
|
# data is converted to velocity data by differentiating the absolute pose measurements. These velocities are then
|
||||||
|
# integrated as usual. NOTE: this only applies to sensors that provide pose measurements; setting differential to true
|
||||||
|
# for twist measurements has no effect.
|
||||||
|
odom0_differential: true
|
||||||
|
|
||||||
|
# [ADVANCED] When the node starts, if this parameter is true, then the first measurement is treated as a "zero point"
|
||||||
|
# for all future measurements. While you can achieve the same effect with the differential paremeter, the key
|
||||||
|
# difference is that the relative parameter doesn't cause the measurement to be converted to a velocity before
|
||||||
|
# integrating it. If you simply want your measurements to start at 0 for a given sensor, set this to true.
|
||||||
|
odom0_relative: false
|
||||||
|
|
||||||
|
# [ADVANCED] If your data is subject to outliers, use these threshold settings, expressed as Mahalanobis distances, to
|
||||||
|
# control how far away from the current vehicle state a sensor measurement is permitted to be. Each defaults to
|
||||||
|
# numeric_limits<double>::max() if unspecified. It is strongly recommended that these parameters be removed if not
|
||||||
|
# required. Data is specified at the level of pose and twist variables, rather than for each variable in isolation.
|
||||||
|
# For messages that have both pose and twist data, the parameter specifies to which part of the message we are applying
|
||||||
|
# the thresholds.
|
||||||
|
odom0_pose_rejection_threshold: 5.0
|
||||||
|
odom0_twist_rejection_threshold: 1.0
|
||||||
|
|
||||||
|
# Further input parameter examples
|
||||||
|
odom1: example/odom2
|
||||||
|
odom1_config: [false, false, true,
|
||||||
|
false, false, false,
|
||||||
|
false, false, false,
|
||||||
|
false, false, true,
|
||||||
|
false, false, false]
|
||||||
|
odom1_differential: false
|
||||||
|
odom1_relative: true
|
||||||
|
odom1_queue_size: 2
|
||||||
|
odom1_pose_rejection_threshold: 2.0
|
||||||
|
odom1_twist_rejection_threshold: 0.2
|
||||||
|
odom1_nodelay: false
|
||||||
|
pose0: example/pose
|
||||||
|
pose0_config: [true, true, false,
|
||||||
|
false, false, false,
|
||||||
|
false, false, false,
|
||||||
|
false, false, false,
|
||||||
|
false, false, false]
|
||||||
|
pose0_differential: true
|
||||||
|
pose0_relative: false
|
||||||
|
pose0_queue_size: 5
|
||||||
|
pose0_rejection_threshold: 2.0 # Note the difference in parameter name
|
||||||
|
pose0_nodelay: false
|
||||||
|
|
||||||
|
twist0: example/twist
|
||||||
|
twist0_config: [false, false, false,
|
||||||
|
false, false, false,
|
||||||
|
true, true, true,
|
||||||
|
false, false, false,
|
||||||
|
false, false, false]
|
||||||
|
twist0_queue_size: 3
|
||||||
|
twist0_rejection_threshold: 2.0
|
||||||
|
twist0_nodelay: false
|
||||||
|
|
||||||
|
imu0: /imu0
|
||||||
|
imu0_config: [false, false, false,
|
||||||
|
true, true, true,
|
||||||
|
false, false, false,
|
||||||
|
true, true, true,
|
||||||
|
false, false, false]
|
||||||
|
#imu0_nodelay: false
|
||||||
|
imu0_differential: true
|
||||||
|
imu0_relative: false
|
||||||
|
imu0_queue_size: 1
|
||||||
|
imu0_pose_rejection_threshold: 0.8 # Note the difference in parameter names
|
||||||
|
imu0_twist_rejection_threshold: 0.8 #
|
||||||
|
imu0_linear_acceleration_rejection_threshold: 0.8 #
|
||||||
|
|
||||||
|
# [ADVANCED] Some IMUs automatically remove acceleration due to gravity, and others don't. If yours doesn't, please set
|
||||||
|
# this to true, and *make sure* your data conforms to REP-103, specifically, that the data is in ENU frame.
|
||||||
|
imu0_remove_gravitational_acceleration: true
|
||||||
|
|
||||||
|
# [ADVANCED] The EKF and UKF models follow a standard predict/correct cycle. During prediction, if there is no
|
||||||
|
# acceleration reference, the velocity at time t+1 is simply predicted to be the same as the velocity at time t. During
|
||||||
|
# correction, this predicted value is fused with the measured value to produce the new velocity estimate. This can be
|
||||||
|
# problematic, as the final velocity will effectively be a weighted average of the old velocity and the new one. When
|
||||||
|
# this velocity is the integrated into a new pose, the result can be sluggish covergence. This effect is especially
|
||||||
|
# noticeable with LIDAR data during rotations. To get around it, users can try inflating the process_noise_covariance
|
||||||
|
# for the velocity variable in question, or decrease the variance of the variable in question in the measurement
|
||||||
|
# itself. In addition, users can also take advantage of the control command being issued to the robot at the time we
|
||||||
|
# make the prediction. If control is used, it will get converted into an acceleration term, which will be used during
|
||||||
|
# predicition. Note that if an acceleration measurement for the variable in question is available from one of the
|
||||||
|
# inputs, the control term will be ignored.
|
||||||
|
# Whether or not we use the control input during predicition. Defaults to false.
|
||||||
|
use_control: false
|
||||||
|
# Whether the input (assumed to be cmd_vel) is a geometry_msgs/Twist or geometry_msgs/TwistStamped message. Defaults to
|
||||||
|
# false.
|
||||||
|
stamped_control: false
|
||||||
|
# The last issued control command will be used in prediction for this period. Defaults to 0.2.
|
||||||
|
control_timeout: 0.2
|
||||||
|
# Which velocities are being controlled. Order is vx, vy, vz, vroll, vpitch, vyaw.
|
||||||
|
control_config: [true, false, false, false, false, true]
|
||||||
|
# Places limits on how large the acceleration term will be. Should match your robot's kinematics.
|
||||||
|
acceleration_limits: [1.3, 0.0, 0.0, 0.0, 0.0, 3.4]
|
||||||
|
# Acceleration and deceleration limits are not always the same for robots.
|
||||||
|
deceleration_limits: [1.3, 0.0, 0.0, 0.0, 0.0, 4.5]
|
||||||
|
# If your robot cannot instantaneously reach its acceleration limit, the permitted change can be controlled with these
|
||||||
|
# gains
|
||||||
|
acceleration_gains: [0.8, 0.0, 0.0, 0.0, 0.0, 0.9]
|
||||||
|
# If your robot cannot instantaneously reach its deceleration limit, the permitted change can be controlled with these
|
||||||
|
# gains
|
||||||
|
deceleration_gains: [1.0, 0.0, 0.0, 0.0, 0.0, 1.0]
|
||||||
|
# [ADVANCED] The process noise covariance matrix can be difficult to tune, and can vary for each application, so it is
|
||||||
|
# exposed as a configuration parameter. This matrix represents the noise we add to the total error after each
|
||||||
|
# prediction step. The better the omnidirectional motion model matches your system, the smaller these values can be.
|
||||||
|
# However, if users find that a given variable is slow to converge, one approach is to increase the
|
||||||
|
# process_noise_covariance diagonal value for the variable in question, which will cause the filter's predicted error
|
||||||
|
# to be larger, which will cause the filter to trust the incoming measurement more during correction. The values are
|
||||||
|
# ordered as x, y, z, roll, pitch, yaw, vx, vy, vz, vroll, vpitch, vyaw, ax, ay, az. Defaults to the matrix below if
|
||||||
|
# unspecified.
|
||||||
|
process_noise_covariance: [0.05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.06, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.03, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.03, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.06, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.025, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.025, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.04, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.02, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.015]
|
||||||
|
# [ADVANCED] This represents the initial value for the state estimate error covariance matrix. Setting a diagonal
|
||||||
|
# value (variance) to a large value will result in rapid convergence for initial measurements of the variable in
|
||||||
|
# question. Users should take care not to use large values for variables that will not be measured directly. The values
|
||||||
|
# are ordered as x, y, z, roll, pitch, yaw, vx, vy, vz, vroll, vpitch, vyaw, ax, ay, az. Defaults to the matrix below
|
||||||
|
#if unspecified.
|
||||||
|
initial_estimate_covariance: [1e-9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 1e-9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 1e-9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 1e-9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 1e-9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.0, 1e-9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1e-9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1e-9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1e-9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1e-9, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1e-9, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1e-9, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1e-9, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1e-9, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1e-9]
|
||||||
|
|
||||||
|
|
@ -0,0 +1,253 @@
|
||||||
|
### ekf config file ###
|
||||||
|
ekf_filter_node:
|
||||||
|
ros__parameters:
|
||||||
|
# The frequency, in Hz, at which the filter will output a position estimate. Note that the filter will not begin
|
||||||
|
# computation until it receives at least one message from one of the inputs. It will then run continuously at the
|
||||||
|
# frequency specified here, regardless of whether it receives more measurements. Defaults to 30 if unspecified.
|
||||||
|
frequency: 30.0
|
||||||
|
|
||||||
|
# The period, in seconds, after which we consider a sensor to have timed out. In this event, we carry out a predict
|
||||||
|
# cycle on the EKF without correcting it. This parameter can be thought of as the minimum frequency with which the
|
||||||
|
# filter will generate new output. Defaults to 1 / frequency if not specified.
|
||||||
|
sensor_timeout: 0.1
|
||||||
|
|
||||||
|
# ekf_localization_node and ukf_localization_node both use a 3D omnidirectional motion model. If this parameter is
|
||||||
|
# set to true, no 3D information will be used in your state estimate. Use this if you are operating in a planar
|
||||||
|
# environment and want to ignore the effect of small variations in the ground plane that might otherwise be detected
|
||||||
|
# by, for example, an IMU. Defaults to false if unspecified.
|
||||||
|
two_d_mode: false
|
||||||
|
|
||||||
|
# Use this parameter to provide an offset to the transform generated by ekf_localization_node. This can be used for
|
||||||
|
# future dating the transform, which is required for interaction with some other packages. Defaults to 0.0 if
|
||||||
|
# unspecified.
|
||||||
|
transform_time_offset: 0.0
|
||||||
|
|
||||||
|
# Use this parameter to provide specify how long the tf listener should wait for a transform to become available.
|
||||||
|
# Defaults to 0.0 if unspecified.
|
||||||
|
transform_timeout: 0.0
|
||||||
|
|
||||||
|
# If you're having trouble, try setting this to true, and then echo the /diagnostics_agg topic to see if the node is
|
||||||
|
# unhappy with any settings or data.
|
||||||
|
print_diagnostics: true
|
||||||
|
|
||||||
|
# Debug settings. Not for the faint of heart. Outputs a ludicrous amount of information to the file specified by
|
||||||
|
# debug_out_file. I hope you like matrices! Please note that setting this to true will have strongly deleterious
|
||||||
|
# effects on the performance of the node. Defaults to false if unspecified.
|
||||||
|
debug: false
|
||||||
|
|
||||||
|
# Defaults to "robot_localization_debug.txt" if unspecified. Please specify the full path.
|
||||||
|
debug_out_file: /path/to/debug/file.txt
|
||||||
|
|
||||||
|
# Whether we'll allow old measurements to cause a re-publication of the updated state
|
||||||
|
permit_corrected_publication: false
|
||||||
|
|
||||||
|
# Whether to publish the acceleration state. Defaults to false if unspecified.
|
||||||
|
publish_acceleration: false
|
||||||
|
|
||||||
|
# Whether to broadcast the transformation over the /tf topic. Defaults to true if unspecified.
|
||||||
|
publish_tf: true
|
||||||
|
|
||||||
|
# REP-105 (http://www.ros.org/reps/rep-0105.html) specifies four principal coordinate frames: base_link, odom, map, and
|
||||||
|
# earth. base_link is the coordinate frame that is affixed to the robot. Both odom and map are world-fixed frames.
|
||||||
|
# The robot's position in the odom frame will drift over time, but is accurate in the short term and should be
|
||||||
|
# continuous. The odom frame is therefore the best frame for executing local motion plans. The map frame, like the odom
|
||||||
|
# frame, is a world-fixed coordinate frame, and while it contains the most globally accurate position estimate for your
|
||||||
|
# robot, it is subject to discrete jumps, e.g., due to the fusion of GPS data or a correction from a map-based
|
||||||
|
# localization node. The earth frame is used to relate multiple map frames by giving them a common reference frame.
|
||||||
|
# ekf_localization_node and ukf_localization_node are not concerned with the earth frame.
|
||||||
|
# Here is how to use the following settings:
|
||||||
|
# 1. Set the map_frame, odom_frame, and base_link frames to the appropriate frame names for your system.
|
||||||
|
# 1a. If your system does not have a map_frame, just remove it, and make sure "world_frame" is set to the value of
|
||||||
|
# odom_frame.
|
||||||
|
# 2. If you are fusing continuous position data such as wheel encoder odometry, visual odometry, or IMU data, set
|
||||||
|
# "world_frame" to your odom_frame value. This is the default behavior for robot_localization's state estimation nodes.
|
||||||
|
# 3. If you are fusing global absolute position data that is subject to discrete jumps (e.g., GPS or position updates
|
||||||
|
# from landmark observations) then:
|
||||||
|
# 3a. Set your "world_frame" to your map_frame value
|
||||||
|
# 3b. MAKE SURE something else is generating the odom->base_link transform. Note that this can even be another state
|
||||||
|
# estimation node from robot_localization! However, that instance should *not* fuse the global data.
|
||||||
|
map_frame: map # Defaults to "map" if unspecified
|
||||||
|
odom_frame: odom # Defaults to "odom" if unspecified
|
||||||
|
base_link_frame: base_link # Defaults to "base_link" if unspecified
|
||||||
|
world_frame: odom # Defaults to the value of odom_frame if unspecified
|
||||||
|
|
||||||
|
# The filter accepts an arbitrary number of inputs from each input message type (nav_msgs/Odometry,
|
||||||
|
# geometry_msgs/PoseWithCovarianceStamped, geometry_msgs/TwistWithCovarianceStamped,
|
||||||
|
# sensor_msgs/Imu). To add an input, simply append the next number in the sequence to its "base" name, e.g., odom0,
|
||||||
|
# odom1, twist0, twist1, imu0, imu1, imu2, etc. The value should be the topic name. These parameters obviously have no
|
||||||
|
# default values, and must be specified.
|
||||||
|
odom0: example/odom
|
||||||
|
|
||||||
|
# Each sensor reading updates some or all of the filter's state. These options give you greater control over which
|
||||||
|
# values from each measurement are fed to the filter. For example, if you have an odometry message as input, but only
|
||||||
|
# want to use its Z position value, then set the entire vector to false, except for the third entry. The order of the
|
||||||
|
# values is x, y, z, roll, pitch, yaw, vx, vy, vz, vroll, vpitch, vyaw, ax, ay, az. Note that not some message types
|
||||||
|
# do not provide some of the state variables estimated by the filter. For example, a TwistWithCovarianceStamped message
|
||||||
|
# has no pose information, so the first six values would be meaningless in that case. Each vector defaults to all false
|
||||||
|
# if unspecified, effectively making this parameter required for each sensor.
|
||||||
|
odom0_config: [true, true, false,
|
||||||
|
false, false, false,
|
||||||
|
false, false, false,
|
||||||
|
false, false, true,
|
||||||
|
false, false, false]
|
||||||
|
|
||||||
|
# If you have high-frequency data or are running with a low frequency parameter value, then you may want to increase
|
||||||
|
# the size of the subscription queue so that more measurements are fused.
|
||||||
|
odom0_queue_size: 2
|
||||||
|
|
||||||
|
# [ADVANCED] Large messages in ROS can exhibit strange behavior when they arrive at a high frequency. This is a result
|
||||||
|
# of Nagle's algorithm. This option tells the ROS subscriber to use the tcpNoDelay option, which disables Nagle's
|
||||||
|
# algorithm.
|
||||||
|
odom0_nodelay: false
|
||||||
|
|
||||||
|
# [ADVANCED] When measuring one pose variable with two sensors, a situation can arise in which both sensors under-
|
||||||
|
# report their covariances. This can lead to the filter rapidly jumping back and forth between each measurement as they
|
||||||
|
# arrive. In these cases, it often makes sense to (a) correct the measurement covariances, or (b) if velocity is also
|
||||||
|
# measured by one of the sensors, let one sensor measure pose, and the other velocity. However, doing (a) or (b) isn't
|
||||||
|
# always feasible, and so we expose the differential parameter. When differential mode is enabled, all absolute pose
|
||||||
|
# data is converted to velocity data by differentiating the absolute pose measurements. These velocities are then
|
||||||
|
# integrated as usual. NOTE: this only applies to sensors that provide pose measurements; setting differential to true
|
||||||
|
# for twist measurements has no effect.
|
||||||
|
odom0_differential: false
|
||||||
|
|
||||||
|
# [ADVANCED] When the node starts, if this parameter is true, then the first measurement is treated as a "zero point"
|
||||||
|
# for all future measurements. While you can achieve the same effect with the differential paremeter, the key
|
||||||
|
# difference is that the relative parameter doesn't cause the measurement to be converted to a velocity before
|
||||||
|
# integrating it. If you simply want your measurements to start at 0 for a given sensor, set this to true.
|
||||||
|
odom0_relative: false
|
||||||
|
|
||||||
|
# [ADVANCED] If your data is subject to outliers, use these threshold settings, expressed as Mahalanobis distances, to
|
||||||
|
# control how far away from the current vehicle state a sensor measurement is permitted to be. Each defaults to
|
||||||
|
# numeric_limits<double>::max() if unspecified. It is strongly recommended that these parameters be removed if not
|
||||||
|
# required. Data is specified at the level of pose and twist variables, rather than for each variable in isolation.
|
||||||
|
# For messages that have both pose and twist data, the parameter specifies to which part of the message we are applying
|
||||||
|
# the thresholds.
|
||||||
|
odom0_pose_rejection_threshold: 5.0
|
||||||
|
odom0_twist_rejection_threshold: 1.0
|
||||||
|
|
||||||
|
# Further input parameter examples
|
||||||
|
odom1: example/odom2
|
||||||
|
odom1_config: [false, false, true,
|
||||||
|
false, false, false,
|
||||||
|
false, false, false,
|
||||||
|
false, false, true,
|
||||||
|
false, false, false]
|
||||||
|
odom1_differential: false
|
||||||
|
odom1_relative: true
|
||||||
|
odom1_queue_size: 2
|
||||||
|
odom1_pose_rejection_threshold: 2.0
|
||||||
|
odom1_twist_rejection_threshold: 0.2
|
||||||
|
odom1_nodelay: false
|
||||||
|
pose0: example/pose
|
||||||
|
pose0_config: [true, true, false,
|
||||||
|
false, false, false,
|
||||||
|
false, false, false,
|
||||||
|
false, false, false,
|
||||||
|
false, false, false]
|
||||||
|
pose0_differential: true
|
||||||
|
pose0_relative: false
|
||||||
|
pose0_queue_size: 5
|
||||||
|
pose0_rejection_threshold: 2.0 # Note the difference in parameter name
|
||||||
|
pose0_nodelay: false
|
||||||
|
|
||||||
|
twist0: example/twist
|
||||||
|
twist0_config: [false, false, false,
|
||||||
|
false, false, false,
|
||||||
|
true, true, true,
|
||||||
|
false, false, false,
|
||||||
|
false, false, false]
|
||||||
|
twist0_queue_size: 3
|
||||||
|
twist0_rejection_threshold: 2.0
|
||||||
|
twist0_nodelay: false
|
||||||
|
|
||||||
|
imu0: example/imu
|
||||||
|
imu0_config: [false, false, false,
|
||||||
|
true, true, true,
|
||||||
|
false, false, false,
|
||||||
|
true, true, true,
|
||||||
|
true, true, true]
|
||||||
|
imu0_nodelay: false
|
||||||
|
imu0_differential: false
|
||||||
|
imu0_relative: true
|
||||||
|
imu0_queue_size: 5
|
||||||
|
imu0_pose_rejection_threshold: 0.8 # Note the difference in parameter names
|
||||||
|
imu0_twist_rejection_threshold: 0.8 #
|
||||||
|
imu0_linear_acceleration_rejection_threshold: 0.8 #
|
||||||
|
|
||||||
|
# [ADVANCED] Some IMUs automatically remove acceleration due to gravity, and others don't. If yours doesn't, please set
|
||||||
|
# this to true, and *make sure* your data conforms to REP-103, specifically, that the data is in ENU frame.
|
||||||
|
imu0_remove_gravitational_acceleration: true
|
||||||
|
|
||||||
|
# [ADVANCED] The EKF and UKF models follow a standard predict/correct cycle. During prediction, if there is no
|
||||||
|
# acceleration reference, the velocity at time t+1 is simply predicted to be the same as the velocity at time t. During
|
||||||
|
# correction, this predicted value is fused with the measured value to produce the new velocity estimate. This can be
|
||||||
|
# problematic, as the final velocity will effectively be a weighted average of the old velocity and the new one. When
|
||||||
|
# this velocity is the integrated into a new pose, the result can be sluggish covergence. This effect is especially
|
||||||
|
# noticeable with LIDAR data during rotations. To get around it, users can try inflating the process_noise_covariance
|
||||||
|
# for the velocity variable in question, or decrease the variance of the variable in question in the measurement
|
||||||
|
# itself. In addition, users can also take advantage of the control command being issued to the robot at the time we
|
||||||
|
# make the prediction. If control is used, it will get converted into an acceleration term, which will be used during
|
||||||
|
# predicition. Note that if an acceleration measurement for the variable in question is available from one of the
|
||||||
|
# inputs, the control term will be ignored.
|
||||||
|
# Whether or not we use the control input during predicition. Defaults to false.
|
||||||
|
use_control: true
|
||||||
|
# Whether the input (assumed to be cmd_vel) is a geometry_msgs/Twist or geometry_msgs/TwistStamped message. Defaults to
|
||||||
|
# false.
|
||||||
|
stamped_control: false
|
||||||
|
# The last issued control command will be used in prediction for this period. Defaults to 0.2.
|
||||||
|
control_timeout: 0.2
|
||||||
|
# Which velocities are being controlled. Order is vx, vy, vz, vroll, vpitch, vyaw.
|
||||||
|
control_config: [true, false, false, false, false, true]
|
||||||
|
# Places limits on how large the acceleration term will be. Should match your robot's kinematics.
|
||||||
|
acceleration_limits: [1.3, 0.0, 0.0, 0.0, 0.0, 3.4]
|
||||||
|
# Acceleration and deceleration limits are not always the same for robots.
|
||||||
|
deceleration_limits: [1.3, 0.0, 0.0, 0.0, 0.0, 4.5]
|
||||||
|
# If your robot cannot instantaneously reach its acceleration limit, the permitted change can be controlled with these
|
||||||
|
# gains
|
||||||
|
acceleration_gains: [0.8, 0.0, 0.0, 0.0, 0.0, 0.9]
|
||||||
|
# If your robot cannot instantaneously reach its deceleration limit, the permitted change can be controlled with these
|
||||||
|
# gains
|
||||||
|
deceleration_gains: [1.0, 0.0, 0.0, 0.0, 0.0, 1.0]
|
||||||
|
# [ADVANCED] The process noise covariance matrix can be difficult to tune, and can vary for each application, so it is
|
||||||
|
# exposed as a configuration parameter. This matrix represents the noise we add to the total error after each
|
||||||
|
# prediction step. The better the omnidirectional motion model matches your system, the smaller these values can be.
|
||||||
|
# However, if users find that a given variable is slow to converge, one approach is to increase the
|
||||||
|
# process_noise_covariance diagonal value for the variable in question, which will cause the filter's predicted error
|
||||||
|
# to be larger, which will cause the filter to trust the incoming measurement more during correction. The values are
|
||||||
|
# ordered as x, y, z, roll, pitch, yaw, vx, vy, vz, vroll, vpitch, vyaw, ax, ay, az. Defaults to the matrix below if
|
||||||
|
# unspecified.
|
||||||
|
process_noise_covariance: [0.05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.06, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.03, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.03, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.06, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.025, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.025, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.04, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.02, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.015]
|
||||||
|
# [ADVANCED] This represents the initial value for the state estimate error covariance matrix. Setting a diagonal
|
||||||
|
# value (variance) to a large value will result in rapid convergence for initial measurements of the variable in
|
||||||
|
# question. Users should take care not to use large values for variables that will not be measured directly. The values
|
||||||
|
# are ordered as x, y, z, roll, pitch, yaw, vx, vy, vz, vroll, vpitch, vyaw, ax, ay, az. Defaults to the matrix below
|
||||||
|
#if unspecified.
|
||||||
|
initial_estimate_covariance: [1e-9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 1e-9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 1e-9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 1e-9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 1e-9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.0, 1e-9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1e-9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1e-9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1e-9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1e-9, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1e-9, 0.0, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1e-9, 0.0, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1e-9, 0.0, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1e-9, 0.0,
|
||||||
|
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1e-9]
|
||||||
|
|
@ -30,7 +30,7 @@ class yahboomcar_driver(Node):
|
||||||
global car_type_dic
|
global car_type_dic
|
||||||
self.RA2DE = 180 / pi
|
self.RA2DE = 180 / pi
|
||||||
# self.car = Rosmaster()
|
# self.car = Rosmaster()
|
||||||
self.car = Rosmaster(1, "/dev/ttyUSB1", 2, False);
|
self.car = Rosmaster(1, "/dev/ttyUSB0", 2, False);
|
||||||
self.car.set_car_type(1)
|
self.car.set_car_type(1)
|
||||||
#get parameter
|
#get parameter
|
||||||
self.declare_parameter('car_type', 'X3')
|
self.declare_parameter('car_type', 'X3')
|
||||||
|
|
|
||||||
Binary file not shown.
Loading…
Reference in New Issue