can_state_estimator

Package Summary

Version	0.0.0
License	Apache 2.0
Build type	AMENT_CMAKE
Use	RECOMMENDED

Repository Summary

Description	Dockerized ROS2 stack for the WATonomous Autonomous Driving Software Pipeline
Checkout URI	https://github.com/watonomous/wato_monorepo.git
VCS Type	git
VCS Version	main
Last Updated	2026-02-24
Dev Status	UNKNOWN
Released	UNRELEASED
Contributing	Help Wanted (-) Good First Issues (-) Pull Requests to Review (-)

Package Description

Node to estimate vehicle state (steering angle, velocity, odometry) from CAN bus and wheel speed data

Maintainers

WATonomous

Authors

No additional authors.

can_state_estimator

Vehicle state estimation from the CAN bus.

This node reads steering angle and wheel speed frames directly from SocketCAN and publishes steering angle, body velocity, and dead-reckoning odometry. It replaces the separate car_steering_feedback and car_velocity_feedback packages with a single lifecycle node.

Node

can_state_estimator_node

Reads two CAN frame types from the vehicle OBD bus:

0x2B0 - Steering wheel angle (int16 LE, 0.1 deg/bit). Converted to wheel angle in radians using the steering_conversion_factor.
0x4B0 - Wheel speeds (four 12-bit values at 2-byte offsets, decoded as (int)(raw / 3.2) / 10.0 in km/h).

Body velocity is computed as the average front wheel speed projected through the steering angle (Ackermann bicycle model). Odometry integrates this velocity and yaw rate over time.

The wheelbase is resolved from TF by looking up the distance between the rear_axle_frame and front_axle_frame (published by robot_state_publisher from the URDF). The node will wait for this transform before publishing velocity or odometry.

ros2 run can_state_estimator can_state_estimator_node

Publications:

Topic	Type	Description
`can_state_estimator/steering_angle`	`roscco_msg/SteeringAngle`	Wheel angle in radians
`can_state_estimator/body_velocity`	`std_msgs/Float64`	Rear-axle longitudinal velocity in m/s
`can_state_estimator/odom`	`nav_msgs/Odometry`	Dead-reckoning pose and twist

Parameters:

Parameter	Type	Default	Description
`can_interface`	string	`can1`	SocketCAN interface for the vehicle OBD bus
`steering_conversion_factor`	double	`15.7`	Steering wheel to wheel angle ratio
`rear_axle_frame`	string	`rear_axle`	TF frame at the rear axle (wheelbase source)
`front_axle_frame`	string	`front_axle`	TF frame at the front axle (wheelbase target)
`odom_frame`	string	`odom`	Frame ID for the odometry header
`base_frame`	string	`base_footprint`	Child frame ID for the odometry message

Lifecycle

The node is managed by wato_lifecycle_manager:

Transition	Action
configure	Read parameters, create publishers, open and bind CAN socket, start TF listener
activate	Activate publishers, reset odometry, start CAN read thread
deactivate	Stop CAN read thread, deactivate publishers
cleanup	Close CAN socket, destroy publishers and TF resources

Odometry Model

Uses the Ackermann bicycle model referenced at the rear axle:

v_front_avg = (v_nw + v_ne) / 2          front wheel average (km/h -> m/s)
v_body      = v_front_avg * cos(delta)    longitudinal velocity at rear axle
omega       = v_body * tan(delta) / L     yaw rate (L = wheelbase from TF)

x     += v_body * cos(theta) * dt
y     += v_body * sin(theta) * dt
theta += omega * dt

This is pure dead-reckoning and will drift over time. Fuse with GPS/IMU for absolute positioning.

CHANGELOG

No CHANGELOG found.

Package Dependencies

Deps	Name
	ament_cmake
	rclcpp
	rclcpp_lifecycle
	roscco_msg
	std_msgs
	nav_msgs
	tf2
	tf2_ros

System Dependencies

No direct system dependencies.

Dependant Packages

Name	Deps
interfacing_bringup

Launch files

No launch files found

Messages

No message files found.

Services

No service files found

Plugins

No plugins found.

Recent questions tagged `can_state_estimator` at Robotics Stack Exchange

No version for distro jazzy showing github. Known supported distros are highlighted in the buttons above.

can_state_estimator package from wato_monorepo repo

action_bringup behaviour_planning global_planning local_planning model_predictive_control wato_trajectory_msgs infrastructure_bringup tf_static_relay vision_msgs_markers wato_lifecycle_manager ackermann_mux camera_calib lidar_lidar_calib can_state_estimator dummy_ackermann eve_description interfacing_bringup joystick_interfacing oscc_interfacing pid_control roscco_msg sensor_interfacing camera_object_detection depth_estimation feature_extraction patchworkpp patchworkpp_vendor perception_bringup perception_utils bytetrack_cpp_vendor track_viz_2d tracking traffic_light_detection carla_bringup carla_common carla_control carla_lifecycle carla_localization carla_msgs carla_perception carla_pygame carla_ros_bridge carla_sample_description carla_scenarios carla_teleop simulation_bringup wato_test behaviortree_ros2_vendor behaviour behaviour_markers behaviour_msgs costmap costmap_markers costmap_msgs lanelet_markers lanelet_msgs lidar_localization localization prediction simple_prediction world_model world_model_markers world_model_msgs world_modeling_bringup

ROS Distro
github

Package Summary

Version	0.0.0
License	Apache 2.0
Build type	AMENT_CMAKE
Use	RECOMMENDED

Repository Summary

Description	Dockerized ROS2 stack for the WATonomous Autonomous Driving Software Pipeline
Checkout URI	https://github.com/watonomous/wato_monorepo.git
VCS Type	git
VCS Version	main
Last Updated	2026-02-24
Dev Status	UNKNOWN
Released	UNRELEASED
Contributing	Help Wanted (-) Good First Issues (-) Pull Requests to Review (-)

Package Description

Node to estimate vehicle state (steering angle, velocity, odometry) from CAN bus and wheel speed data

Maintainers

WATonomous

Authors

No additional authors.

can_state_estimator

Vehicle state estimation from the CAN bus.

This node reads steering angle and wheel speed frames directly from SocketCAN and publishes steering angle, body velocity, and dead-reckoning odometry. It replaces the separate car_steering_feedback and car_velocity_feedback packages with a single lifecycle node.

Node

can_state_estimator_node

Reads two CAN frame types from the vehicle OBD bus:

0x2B0 - Steering wheel angle (int16 LE, 0.1 deg/bit). Converted to wheel angle in radians using the steering_conversion_factor.
0x4B0 - Wheel speeds (four 12-bit values at 2-byte offsets, decoded as (int)(raw / 3.2) / 10.0 in km/h).

Body velocity is computed as the average front wheel speed projected through the steering angle (Ackermann bicycle model). Odometry integrates this velocity and yaw rate over time.

The wheelbase is resolved from TF by looking up the distance between the rear_axle_frame and front_axle_frame (published by robot_state_publisher from the URDF). The node will wait for this transform before publishing velocity or odometry.

ros2 run can_state_estimator can_state_estimator_node

Publications:

Topic	Type	Description
`can_state_estimator/steering_angle`	`roscco_msg/SteeringAngle`	Wheel angle in radians
`can_state_estimator/body_velocity`	`std_msgs/Float64`	Rear-axle longitudinal velocity in m/s
`can_state_estimator/odom`	`nav_msgs/Odometry`	Dead-reckoning pose and twist

Parameters:

Parameter	Type	Default	Description
`can_interface`	string	`can1`	SocketCAN interface for the vehicle OBD bus
`steering_conversion_factor`	double	`15.7`	Steering wheel to wheel angle ratio
`rear_axle_frame`	string	`rear_axle`	TF frame at the rear axle (wheelbase source)
`front_axle_frame`	string	`front_axle`	TF frame at the front axle (wheelbase target)
`odom_frame`	string	`odom`	Frame ID for the odometry header
`base_frame`	string	`base_footprint`	Child frame ID for the odometry message

Lifecycle

The node is managed by wato_lifecycle_manager:

Transition	Action
configure	Read parameters, create publishers, open and bind CAN socket, start TF listener
activate	Activate publishers, reset odometry, start CAN read thread
deactivate	Stop CAN read thread, deactivate publishers
cleanup	Close CAN socket, destroy publishers and TF resources

Odometry Model

Uses the Ackermann bicycle model referenced at the rear axle:

v_front_avg = (v_nw + v_ne) / 2          front wheel average (km/h -> m/s)
v_body      = v_front_avg * cos(delta)    longitudinal velocity at rear axle
omega       = v_body * tan(delta) / L     yaw rate (L = wheelbase from TF)

x     += v_body * cos(theta) * dt
y     += v_body * sin(theta) * dt
theta += omega * dt

This is pure dead-reckoning and will drift over time. Fuse with GPS/IMU for absolute positioning.

CHANGELOG

No CHANGELOG found.

Package Dependencies

Deps	Name
	ament_cmake
	rclcpp
	rclcpp_lifecycle
	roscco_msg
	std_msgs
	nav_msgs
	tf2
	tf2_ros

System Dependencies

No direct system dependencies.

Dependant Packages

Name	Deps
interfacing_bringup

Launch files

No launch files found

Messages

No message files found.

Services

No service files found

Plugins

No plugins found.

Recent questions tagged `can_state_estimator` at Robotics Stack Exchange

No version for distro kilted showing github. Known supported distros are highlighted in the buttons above.

can_state_estimator package from wato_monorepo repo

action_bringup behaviour_planning global_planning local_planning model_predictive_control wato_trajectory_msgs infrastructure_bringup tf_static_relay vision_msgs_markers wato_lifecycle_manager ackermann_mux camera_calib lidar_lidar_calib can_state_estimator dummy_ackermann eve_description interfacing_bringup joystick_interfacing oscc_interfacing pid_control roscco_msg sensor_interfacing camera_object_detection depth_estimation feature_extraction patchworkpp patchworkpp_vendor perception_bringup perception_utils bytetrack_cpp_vendor track_viz_2d tracking traffic_light_detection carla_bringup carla_common carla_control carla_lifecycle carla_localization carla_msgs carla_perception carla_pygame carla_ros_bridge carla_sample_description carla_scenarios carla_teleop simulation_bringup wato_test behaviortree_ros2_vendor behaviour behaviour_markers behaviour_msgs costmap costmap_markers costmap_msgs lanelet_markers lanelet_msgs lidar_localization localization prediction simple_prediction world_model world_model_markers world_model_msgs world_modeling_bringup

ROS Distro
github

Package Summary

Version	0.0.0
License	Apache 2.0
Build type	AMENT_CMAKE
Use	RECOMMENDED

Repository Summary

Description	Dockerized ROS2 stack for the WATonomous Autonomous Driving Software Pipeline
Checkout URI	https://github.com/watonomous/wato_monorepo.git
VCS Type	git
VCS Version	main
Last Updated	2026-02-24
Dev Status	UNKNOWN
Released	UNRELEASED
Contributing	Help Wanted (-) Good First Issues (-) Pull Requests to Review (-)

Package Description

Node to estimate vehicle state (steering angle, velocity, odometry) from CAN bus and wheel speed data

Maintainers

WATonomous

Authors

No additional authors.

can_state_estimator

Vehicle state estimation from the CAN bus.

This node reads steering angle and wheel speed frames directly from SocketCAN and publishes steering angle, body velocity, and dead-reckoning odometry. It replaces the separate car_steering_feedback and car_velocity_feedback packages with a single lifecycle node.

Node

can_state_estimator_node

Reads two CAN frame types from the vehicle OBD bus:

0x2B0 - Steering wheel angle (int16 LE, 0.1 deg/bit). Converted to wheel angle in radians using the steering_conversion_factor.
0x4B0 - Wheel speeds (four 12-bit values at 2-byte offsets, decoded as (int)(raw / 3.2) / 10.0 in km/h).

Body velocity is computed as the average front wheel speed projected through the steering angle (Ackermann bicycle model). Odometry integrates this velocity and yaw rate over time.

The wheelbase is resolved from TF by looking up the distance between the rear_axle_frame and front_axle_frame (published by robot_state_publisher from the URDF). The node will wait for this transform before publishing velocity or odometry.

ros2 run can_state_estimator can_state_estimator_node

Publications:

Topic	Type	Description
`can_state_estimator/steering_angle`	`roscco_msg/SteeringAngle`	Wheel angle in radians
`can_state_estimator/body_velocity`	`std_msgs/Float64`	Rear-axle longitudinal velocity in m/s
`can_state_estimator/odom`	`nav_msgs/Odometry`	Dead-reckoning pose and twist

Parameters:

Parameter	Type	Default	Description
`can_interface`	string	`can1`	SocketCAN interface for the vehicle OBD bus
`steering_conversion_factor`	double	`15.7`	Steering wheel to wheel angle ratio
`rear_axle_frame`	string	`rear_axle`	TF frame at the rear axle (wheelbase source)
`front_axle_frame`	string	`front_axle`	TF frame at the front axle (wheelbase target)
`odom_frame`	string	`odom`	Frame ID for the odometry header
`base_frame`	string	`base_footprint`	Child frame ID for the odometry message

Lifecycle

The node is managed by wato_lifecycle_manager:

Transition	Action
configure	Read parameters, create publishers, open and bind CAN socket, start TF listener
activate	Activate publishers, reset odometry, start CAN read thread
deactivate	Stop CAN read thread, deactivate publishers
cleanup	Close CAN socket, destroy publishers and TF resources

Odometry Model

Uses the Ackermann bicycle model referenced at the rear axle:

v_front_avg = (v_nw + v_ne) / 2          front wheel average (km/h -> m/s)
v_body      = v_front_avg * cos(delta)    longitudinal velocity at rear axle
omega       = v_body * tan(delta) / L     yaw rate (L = wheelbase from TF)

x     += v_body * cos(theta) * dt
y     += v_body * sin(theta) * dt
theta += omega * dt

This is pure dead-reckoning and will drift over time. Fuse with GPS/IMU for absolute positioning.

CHANGELOG

No CHANGELOG found.

Package Dependencies

Deps	Name
	ament_cmake
	rclcpp
	rclcpp_lifecycle
	roscco_msg
	std_msgs
	nav_msgs
	tf2
	tf2_ros

System Dependencies

No direct system dependencies.

Dependant Packages

Name	Deps
interfacing_bringup

Launch files

No launch files found

Messages

No message files found.

Services

No service files found

Plugins

No plugins found.

Recent questions tagged `can_state_estimator` at Robotics Stack Exchange

No version for distro rolling showing github. Known supported distros are highlighted in the buttons above.

can_state_estimator package from wato_monorepo repo

action_bringup behaviour_planning global_planning local_planning model_predictive_control wato_trajectory_msgs infrastructure_bringup tf_static_relay vision_msgs_markers wato_lifecycle_manager ackermann_mux camera_calib lidar_lidar_calib can_state_estimator dummy_ackermann eve_description interfacing_bringup joystick_interfacing oscc_interfacing pid_control roscco_msg sensor_interfacing camera_object_detection depth_estimation feature_extraction patchworkpp patchworkpp_vendor perception_bringup perception_utils bytetrack_cpp_vendor track_viz_2d tracking traffic_light_detection carla_bringup carla_common carla_control carla_lifecycle carla_localization carla_msgs carla_perception carla_pygame carla_ros_bridge carla_sample_description carla_scenarios carla_teleop simulation_bringup wato_test behaviortree_ros2_vendor behaviour behaviour_markers behaviour_msgs costmap costmap_markers costmap_msgs lanelet_markers lanelet_msgs lidar_localization localization prediction simple_prediction world_model world_model_markers world_model_msgs world_modeling_bringup

ROS Distro
github

Package Summary

Version	0.0.0
License	Apache 2.0
Build type	AMENT_CMAKE
Use	RECOMMENDED

Repository Summary

Description	Dockerized ROS2 stack for the WATonomous Autonomous Driving Software Pipeline
Checkout URI	https://github.com/watonomous/wato_monorepo.git
VCS Type	git
VCS Version	main
Last Updated	2026-02-24
Dev Status	UNKNOWN
Released	UNRELEASED
Contributing	Help Wanted (-) Good First Issues (-) Pull Requests to Review (-)

Package Description

Node to estimate vehicle state (steering angle, velocity, odometry) from CAN bus and wheel speed data

Maintainers

WATonomous

Authors

No additional authors.

can_state_estimator

Vehicle state estimation from the CAN bus.

This node reads steering angle and wheel speed frames directly from SocketCAN and publishes steering angle, body velocity, and dead-reckoning odometry. It replaces the separate car_steering_feedback and car_velocity_feedback packages with a single lifecycle node.

Node

can_state_estimator_node

Reads two CAN frame types from the vehicle OBD bus:

0x2B0 - Steering wheel angle (int16 LE, 0.1 deg/bit). Converted to wheel angle in radians using the steering_conversion_factor.
0x4B0 - Wheel speeds (four 12-bit values at 2-byte offsets, decoded as (int)(raw / 3.2) / 10.0 in km/h).

Body velocity is computed as the average front wheel speed projected through the steering angle (Ackermann bicycle model). Odometry integrates this velocity and yaw rate over time.

The wheelbase is resolved from TF by looking up the distance between the rear_axle_frame and front_axle_frame (published by robot_state_publisher from the URDF). The node will wait for this transform before publishing velocity or odometry.

ros2 run can_state_estimator can_state_estimator_node

Publications:

Topic	Type	Description
`can_state_estimator/steering_angle`	`roscco_msg/SteeringAngle`	Wheel angle in radians
`can_state_estimator/body_velocity`	`std_msgs/Float64`	Rear-axle longitudinal velocity in m/s
`can_state_estimator/odom`	`nav_msgs/Odometry`	Dead-reckoning pose and twist

Parameters:

Parameter	Type	Default	Description
`can_interface`	string	`can1`	SocketCAN interface for the vehicle OBD bus
`steering_conversion_factor`	double	`15.7`	Steering wheel to wheel angle ratio
`rear_axle_frame`	string	`rear_axle`	TF frame at the rear axle (wheelbase source)
`front_axle_frame`	string	`front_axle`	TF frame at the front axle (wheelbase target)
`odom_frame`	string	`odom`	Frame ID for the odometry header
`base_frame`	string	`base_footprint`	Child frame ID for the odometry message

Lifecycle

The node is managed by wato_lifecycle_manager:

Transition	Action
configure	Read parameters, create publishers, open and bind CAN socket, start TF listener
activate	Activate publishers, reset odometry, start CAN read thread
deactivate	Stop CAN read thread, deactivate publishers
cleanup	Close CAN socket, destroy publishers and TF resources

Odometry Model

Uses the Ackermann bicycle model referenced at the rear axle:

v_front_avg = (v_nw + v_ne) / 2          front wheel average (km/h -> m/s)
v_body      = v_front_avg * cos(delta)    longitudinal velocity at rear axle
omega       = v_body * tan(delta) / L     yaw rate (L = wheelbase from TF)

x     += v_body * cos(theta) * dt
y     += v_body * sin(theta) * dt
theta += omega * dt

This is pure dead-reckoning and will drift over time. Fuse with GPS/IMU for absolute positioning.

CHANGELOG

No CHANGELOG found.

Package Dependencies

Deps	Name
	ament_cmake
	rclcpp
	rclcpp_lifecycle
	roscco_msg
	std_msgs
	nav_msgs
	tf2
	tf2_ros

System Dependencies

No direct system dependencies.

Dependant Packages

Name	Deps
interfacing_bringup

Launch files

No launch files found

Messages

No message files found.

Services

No service files found

Plugins

No plugins found.

Recent questions tagged `can_state_estimator` at Robotics Stack Exchange

can_state_estimator package from wato_monorepo repo

action_bringup behaviour_planning global_planning local_planning model_predictive_control wato_trajectory_msgs infrastructure_bringup tf_static_relay vision_msgs_markers wato_lifecycle_manager ackermann_mux camera_calib lidar_lidar_calib can_state_estimator dummy_ackermann eve_description interfacing_bringup joystick_interfacing oscc_interfacing pid_control roscco_msg sensor_interfacing camera_object_detection depth_estimation feature_extraction patchworkpp patchworkpp_vendor perception_bringup perception_utils bytetrack_cpp_vendor track_viz_2d tracking traffic_light_detection carla_bringup carla_common carla_control carla_lifecycle carla_localization carla_msgs carla_perception carla_pygame carla_ros_bridge carla_sample_description carla_scenarios carla_teleop simulation_bringup wato_test behaviortree_ros2_vendor behaviour behaviour_markers behaviour_msgs costmap costmap_markers costmap_msgs lanelet_markers lanelet_msgs lidar_localization localization prediction simple_prediction world_model world_model_markers world_model_msgs world_modeling_bringup

ROS Distro
github

Package Summary

Version	0.0.0
License	Apache 2.0
Build type	AMENT_CMAKE
Use	RECOMMENDED

Repository Summary

Description	Dockerized ROS2 stack for the WATonomous Autonomous Driving Software Pipeline
Checkout URI	https://github.com/watonomous/wato_monorepo.git
VCS Type	git
VCS Version	main
Last Updated	2026-02-24
Dev Status	UNKNOWN
Released	UNRELEASED
Contributing	Help Wanted (-) Good First Issues (-) Pull Requests to Review (-)

Package Description

Node to estimate vehicle state (steering angle, velocity, odometry) from CAN bus and wheel speed data

Maintainers

WATonomous

Authors

No additional authors.

can_state_estimator

Vehicle state estimation from the CAN bus.

This node reads steering angle and wheel speed frames directly from SocketCAN and publishes steering angle, body velocity, and dead-reckoning odometry. It replaces the separate car_steering_feedback and car_velocity_feedback packages with a single lifecycle node.

Node

can_state_estimator_node

Reads two CAN frame types from the vehicle OBD bus:

0x2B0 - Steering wheel angle (int16 LE, 0.1 deg/bit). Converted to wheel angle in radians using the steering_conversion_factor.
0x4B0 - Wheel speeds (four 12-bit values at 2-byte offsets, decoded as (int)(raw / 3.2) / 10.0 in km/h).

Body velocity is computed as the average front wheel speed projected through the steering angle (Ackermann bicycle model). Odometry integrates this velocity and yaw rate over time.

The wheelbase is resolved from TF by looking up the distance between the rear_axle_frame and front_axle_frame (published by robot_state_publisher from the URDF). The node will wait for this transform before publishing velocity or odometry.

ros2 run can_state_estimator can_state_estimator_node

Publications:

Topic	Type	Description
`can_state_estimator/steering_angle`	`roscco_msg/SteeringAngle`	Wheel angle in radians
`can_state_estimator/body_velocity`	`std_msgs/Float64`	Rear-axle longitudinal velocity in m/s
`can_state_estimator/odom`	`nav_msgs/Odometry`	Dead-reckoning pose and twist

Parameters:

Parameter	Type	Default	Description
`can_interface`	string	`can1`	SocketCAN interface for the vehicle OBD bus
`steering_conversion_factor`	double	`15.7`	Steering wheel to wheel angle ratio
`rear_axle_frame`	string	`rear_axle`	TF frame at the rear axle (wheelbase source)
`front_axle_frame`	string	`front_axle`	TF frame at the front axle (wheelbase target)
`odom_frame`	string	`odom`	Frame ID for the odometry header
`base_frame`	string	`base_footprint`	Child frame ID for the odometry message

Lifecycle

The node is managed by wato_lifecycle_manager:

Transition	Action
configure	Read parameters, create publishers, open and bind CAN socket, start TF listener
activate	Activate publishers, reset odometry, start CAN read thread
deactivate	Stop CAN read thread, deactivate publishers
cleanup	Close CAN socket, destroy publishers and TF resources

Odometry Model

Uses the Ackermann bicycle model referenced at the rear axle:

v_front_avg = (v_nw + v_ne) / 2          front wheel average (km/h -> m/s)
v_body      = v_front_avg * cos(delta)    longitudinal velocity at rear axle
omega       = v_body * tan(delta) / L     yaw rate (L = wheelbase from TF)

x     += v_body * cos(theta) * dt
y     += v_body * sin(theta) * dt
theta += omega * dt

This is pure dead-reckoning and will drift over time. Fuse with GPS/IMU for absolute positioning.

CHANGELOG

No CHANGELOG found.

Package Dependencies

Deps	Name
	ament_cmake
	rclcpp
	rclcpp_lifecycle
	roscco_msg
	std_msgs
	nav_msgs
	tf2
	tf2_ros

System Dependencies

No direct system dependencies.

Dependant Packages

Name	Deps
interfacing_bringup

Launch files

No launch files found

Messages

No message files found.

Services

No service files found

Plugins

No plugins found.

Recent questions tagged `can_state_estimator` at Robotics Stack Exchange

No version for distro galactic showing github. Known supported distros are highlighted in the buttons above.

can_state_estimator package from wato_monorepo repo

action_bringup behaviour_planning global_planning local_planning model_predictive_control wato_trajectory_msgs infrastructure_bringup tf_static_relay vision_msgs_markers wato_lifecycle_manager ackermann_mux camera_calib lidar_lidar_calib can_state_estimator dummy_ackermann eve_description interfacing_bringup joystick_interfacing oscc_interfacing pid_control roscco_msg sensor_interfacing camera_object_detection depth_estimation feature_extraction patchworkpp patchworkpp_vendor perception_bringup perception_utils bytetrack_cpp_vendor track_viz_2d tracking traffic_light_detection carla_bringup carla_common carla_control carla_lifecycle carla_localization carla_msgs carla_perception carla_pygame carla_ros_bridge carla_sample_description carla_scenarios carla_teleop simulation_bringup wato_test behaviortree_ros2_vendor behaviour behaviour_markers behaviour_msgs costmap costmap_markers costmap_msgs lanelet_markers lanelet_msgs lidar_localization localization prediction simple_prediction world_model world_model_markers world_model_msgs world_modeling_bringup

ROS Distro
github

Package Summary

Version	0.0.0
License	Apache 2.0
Build type	AMENT_CMAKE
Use	RECOMMENDED

Repository Summary

Description	Dockerized ROS2 stack for the WATonomous Autonomous Driving Software Pipeline
Checkout URI	https://github.com/watonomous/wato_monorepo.git
VCS Type	git
VCS Version	main
Last Updated	2026-02-24
Dev Status	UNKNOWN
Released	UNRELEASED
Contributing	Help Wanted (-) Good First Issues (-) Pull Requests to Review (-)

Package Description

Node to estimate vehicle state (steering angle, velocity, odometry) from CAN bus and wheel speed data

Maintainers

WATonomous

Authors

No additional authors.

can_state_estimator

Vehicle state estimation from the CAN bus.

This node reads steering angle and wheel speed frames directly from SocketCAN and publishes steering angle, body velocity, and dead-reckoning odometry. It replaces the separate car_steering_feedback and car_velocity_feedback packages with a single lifecycle node.

Node

can_state_estimator_node

Reads two CAN frame types from the vehicle OBD bus:

0x2B0 - Steering wheel angle (int16 LE, 0.1 deg/bit). Converted to wheel angle in radians using the steering_conversion_factor.
0x4B0 - Wheel speeds (four 12-bit values at 2-byte offsets, decoded as (int)(raw / 3.2) / 10.0 in km/h).

Body velocity is computed as the average front wheel speed projected through the steering angle (Ackermann bicycle model). Odometry integrates this velocity and yaw rate over time.

The wheelbase is resolved from TF by looking up the distance between the rear_axle_frame and front_axle_frame (published by robot_state_publisher from the URDF). The node will wait for this transform before publishing velocity or odometry.

ros2 run can_state_estimator can_state_estimator_node

Publications:

Topic	Type	Description
`can_state_estimator/steering_angle`	`roscco_msg/SteeringAngle`	Wheel angle in radians
`can_state_estimator/body_velocity`	`std_msgs/Float64`	Rear-axle longitudinal velocity in m/s
`can_state_estimator/odom`	`nav_msgs/Odometry`	Dead-reckoning pose and twist

Parameters:

Parameter	Type	Default	Description
`can_interface`	string	`can1`	SocketCAN interface for the vehicle OBD bus
`steering_conversion_factor`	double	`15.7`	Steering wheel to wheel angle ratio
`rear_axle_frame`	string	`rear_axle`	TF frame at the rear axle (wheelbase source)
`front_axle_frame`	string	`front_axle`	TF frame at the front axle (wheelbase target)
`odom_frame`	string	`odom`	Frame ID for the odometry header
`base_frame`	string	`base_footprint`	Child frame ID for the odometry message

Lifecycle

The node is managed by wato_lifecycle_manager:

Transition	Action
configure	Read parameters, create publishers, open and bind CAN socket, start TF listener
activate	Activate publishers, reset odometry, start CAN read thread
deactivate	Stop CAN read thread, deactivate publishers
cleanup	Close CAN socket, destroy publishers and TF resources

Odometry Model

Uses the Ackermann bicycle model referenced at the rear axle:

v_front_avg = (v_nw + v_ne) / 2          front wheel average (km/h -> m/s)
v_body      = v_front_avg * cos(delta)    longitudinal velocity at rear axle
omega       = v_body * tan(delta) / L     yaw rate (L = wheelbase from TF)

x     += v_body * cos(theta) * dt
y     += v_body * sin(theta) * dt
theta += omega * dt

This is pure dead-reckoning and will drift over time. Fuse with GPS/IMU for absolute positioning.

CHANGELOG

No CHANGELOG found.

Package Dependencies

Deps	Name
	ament_cmake
	rclcpp
	rclcpp_lifecycle
	roscco_msg
	std_msgs
	nav_msgs
	tf2
	tf2_ros

System Dependencies

No direct system dependencies.

Dependant Packages

Name	Deps
interfacing_bringup

Launch files

No launch files found

Messages

No message files found.

Services

No service files found

Plugins

No plugins found.

Recent questions tagged `can_state_estimator` at Robotics Stack Exchange

No version for distro iron showing github. Known supported distros are highlighted in the buttons above.

can_state_estimator package from wato_monorepo repo

action_bringup behaviour_planning global_planning local_planning model_predictive_control wato_trajectory_msgs infrastructure_bringup tf_static_relay vision_msgs_markers wato_lifecycle_manager ackermann_mux camera_calib lidar_lidar_calib can_state_estimator dummy_ackermann eve_description interfacing_bringup joystick_interfacing oscc_interfacing pid_control roscco_msg sensor_interfacing camera_object_detection depth_estimation feature_extraction patchworkpp patchworkpp_vendor perception_bringup perception_utils bytetrack_cpp_vendor track_viz_2d tracking traffic_light_detection carla_bringup carla_common carla_control carla_lifecycle carla_localization carla_msgs carla_perception carla_pygame carla_ros_bridge carla_sample_description carla_scenarios carla_teleop simulation_bringup wato_test behaviortree_ros2_vendor behaviour behaviour_markers behaviour_msgs costmap costmap_markers costmap_msgs lanelet_markers lanelet_msgs lidar_localization localization prediction simple_prediction world_model world_model_markers world_model_msgs world_modeling_bringup

ROS Distro
github

Package Summary

Version	0.0.0
License	Apache 2.0
Build type	AMENT_CMAKE
Use	RECOMMENDED

Repository Summary

Description	Dockerized ROS2 stack for the WATonomous Autonomous Driving Software Pipeline
Checkout URI	https://github.com/watonomous/wato_monorepo.git
VCS Type	git
VCS Version	main
Last Updated	2026-02-24
Dev Status	UNKNOWN
Released	UNRELEASED
Contributing	Help Wanted (-) Good First Issues (-) Pull Requests to Review (-)

Package Description

Node to estimate vehicle state (steering angle, velocity, odometry) from CAN bus and wheel speed data

Maintainers

WATonomous

Authors

No additional authors.

can_state_estimator

Vehicle state estimation from the CAN bus.

This node reads steering angle and wheel speed frames directly from SocketCAN and publishes steering angle, body velocity, and dead-reckoning odometry. It replaces the separate car_steering_feedback and car_velocity_feedback packages with a single lifecycle node.

Node

can_state_estimator_node

Reads two CAN frame types from the vehicle OBD bus:

0x2B0 - Steering wheel angle (int16 LE, 0.1 deg/bit). Converted to wheel angle in radians using the steering_conversion_factor.
0x4B0 - Wheel speeds (four 12-bit values at 2-byte offsets, decoded as (int)(raw / 3.2) / 10.0 in km/h).

Body velocity is computed as the average front wheel speed projected through the steering angle (Ackermann bicycle model). Odometry integrates this velocity and yaw rate over time.

The wheelbase is resolved from TF by looking up the distance between the rear_axle_frame and front_axle_frame (published by robot_state_publisher from the URDF). The node will wait for this transform before publishing velocity or odometry.

ros2 run can_state_estimator can_state_estimator_node

Publications:

Topic	Type	Description
`can_state_estimator/steering_angle`	`roscco_msg/SteeringAngle`	Wheel angle in radians
`can_state_estimator/body_velocity`	`std_msgs/Float64`	Rear-axle longitudinal velocity in m/s
`can_state_estimator/odom`	`nav_msgs/Odometry`	Dead-reckoning pose and twist

Parameters:

Parameter	Type	Default	Description
`can_interface`	string	`can1`	SocketCAN interface for the vehicle OBD bus
`steering_conversion_factor`	double	`15.7`	Steering wheel to wheel angle ratio
`rear_axle_frame`	string	`rear_axle`	TF frame at the rear axle (wheelbase source)
`front_axle_frame`	string	`front_axle`	TF frame at the front axle (wheelbase target)
`odom_frame`	string	`odom`	Frame ID for the odometry header
`base_frame`	string	`base_footprint`	Child frame ID for the odometry message

Lifecycle

The node is managed by wato_lifecycle_manager:

Transition	Action
configure	Read parameters, create publishers, open and bind CAN socket, start TF listener
activate	Activate publishers, reset odometry, start CAN read thread
deactivate	Stop CAN read thread, deactivate publishers
cleanup	Close CAN socket, destroy publishers and TF resources

Odometry Model

Uses the Ackermann bicycle model referenced at the rear axle:

v_front_avg = (v_nw + v_ne) / 2          front wheel average (km/h -> m/s)
v_body      = v_front_avg * cos(delta)    longitudinal velocity at rear axle
omega       = v_body * tan(delta) / L     yaw rate (L = wheelbase from TF)

x     += v_body * cos(theta) * dt
y     += v_body * sin(theta) * dt
theta += omega * dt

This is pure dead-reckoning and will drift over time. Fuse with GPS/IMU for absolute positioning.

CHANGELOG

No CHANGELOG found.

Package Dependencies

Deps	Name
	ament_cmake
	rclcpp
	rclcpp_lifecycle
	roscco_msg
	std_msgs
	nav_msgs
	tf2
	tf2_ros

System Dependencies

No direct system dependencies.

Dependant Packages

Name	Deps
interfacing_bringup

Launch files

No launch files found

Messages

No message files found.

Services

No service files found

Plugins

No plugins found.

Recent questions tagged `can_state_estimator` at Robotics Stack Exchange

No version for distro melodic showing github. Known supported distros are highlighted in the buttons above.

can_state_estimator package from wato_monorepo repo

action_bringup behaviour_planning global_planning local_planning model_predictive_control wato_trajectory_msgs infrastructure_bringup tf_static_relay vision_msgs_markers wato_lifecycle_manager ackermann_mux camera_calib lidar_lidar_calib can_state_estimator dummy_ackermann eve_description interfacing_bringup joystick_interfacing oscc_interfacing pid_control roscco_msg sensor_interfacing camera_object_detection depth_estimation feature_extraction patchworkpp patchworkpp_vendor perception_bringup perception_utils bytetrack_cpp_vendor track_viz_2d tracking traffic_light_detection carla_bringup carla_common carla_control carla_lifecycle carla_localization carla_msgs carla_perception carla_pygame carla_ros_bridge carla_sample_description carla_scenarios carla_teleop simulation_bringup wato_test behaviortree_ros2_vendor behaviour behaviour_markers behaviour_msgs costmap costmap_markers costmap_msgs lanelet_markers lanelet_msgs lidar_localization localization prediction simple_prediction world_model world_model_markers world_model_msgs world_modeling_bringup

ROS Distro
github

API Docs
Browse Code
Wiki

Package Summary

Version	0.0.0
License	Apache 2.0
Build type	AMENT_CMAKE
Use	RECOMMENDED

Repository Summary

Description	Dockerized ROS2 stack for the WATonomous Autonomous Driving Software Pipeline
Checkout URI	https://github.com/watonomous/wato_monorepo.git
VCS Type	git
VCS Version	main
Last Updated	2026-02-24
Dev Status	UNKNOWN
Released	UNRELEASED
Contributing	Help Wanted (-) Good First Issues (-) Pull Requests to Review (-)

Package Description

Node to estimate vehicle state (steering angle, velocity, odometry) from CAN bus and wheel speed data

Maintainers

WATonomous

Authors

No additional authors.

can_state_estimator

Vehicle state estimation from the CAN bus.

This node reads steering angle and wheel speed frames directly from SocketCAN and publishes steering angle, body velocity, and dead-reckoning odometry. It replaces the separate car_steering_feedback and car_velocity_feedback packages with a single lifecycle node.

Node

can_state_estimator_node

Reads two CAN frame types from the vehicle OBD bus:

0x2B0 - Steering wheel angle (int16 LE, 0.1 deg/bit). Converted to wheel angle in radians using the steering_conversion_factor.
0x4B0 - Wheel speeds (four 12-bit values at 2-byte offsets, decoded as (int)(raw / 3.2) / 10.0 in km/h).

Body velocity is computed as the average front wheel speed projected through the steering angle (Ackermann bicycle model). Odometry integrates this velocity and yaw rate over time.

The wheelbase is resolved from TF by looking up the distance between the rear_axle_frame and front_axle_frame (published by robot_state_publisher from the URDF). The node will wait for this transform before publishing velocity or odometry.

ros2 run can_state_estimator can_state_estimator_node

Publications:

Topic	Type	Description
`can_state_estimator/steering_angle`	`roscco_msg/SteeringAngle`	Wheel angle in radians
`can_state_estimator/body_velocity`	`std_msgs/Float64`	Rear-axle longitudinal velocity in m/s
`can_state_estimator/odom`	`nav_msgs/Odometry`	Dead-reckoning pose and twist

Parameters:

Parameter	Type	Default	Description
`can_interface`	string	`can1`	SocketCAN interface for the vehicle OBD bus
`steering_conversion_factor`	double	`15.7`	Steering wheel to wheel angle ratio
`rear_axle_frame`	string	`rear_axle`	TF frame at the rear axle (wheelbase source)
`front_axle_frame`	string	`front_axle`	TF frame at the front axle (wheelbase target)
`odom_frame`	string	`odom`	Frame ID for the odometry header
`base_frame`	string	`base_footprint`	Child frame ID for the odometry message

Lifecycle

The node is managed by wato_lifecycle_manager:

Transition	Action
configure	Read parameters, create publishers, open and bind CAN socket, start TF listener
activate	Activate publishers, reset odometry, start CAN read thread
deactivate	Stop CAN read thread, deactivate publishers
cleanup	Close CAN socket, destroy publishers and TF resources

Odometry Model

Uses the Ackermann bicycle model referenced at the rear axle:

v_front_avg = (v_nw + v_ne) / 2          front wheel average (km/h -> m/s)
v_body      = v_front_avg * cos(delta)    longitudinal velocity at rear axle
omega       = v_body * tan(delta) / L     yaw rate (L = wheelbase from TF)

x     += v_body * cos(theta) * dt
y     += v_body * sin(theta) * dt
theta += omega * dt

This is pure dead-reckoning and will drift over time. Fuse with GPS/IMU for absolute positioning.

CHANGELOG

No CHANGELOG found.

Package Dependencies

Deps	Name
	ament_cmake
	rclcpp
	rclcpp_lifecycle
	roscco_msg
	std_msgs
	nav_msgs
	tf2
	tf2_ros

System Dependencies

No direct system dependencies.

Dependant Packages

Name	Deps
interfacing_bringup

Launch files

No launch files found

Messages

No message files found.

Services

No service files found

Plugins

No plugins found.

Recent questions tagged `can_state_estimator` at Robotics Stack Exchange

API Docs Browse Code Wiki

No version for distro noetic showing github. Known supported distros are highlighted in the buttons above.

can_state_estimator package from wato_monorepo repo

action_bringup behaviour_planning global_planning local_planning model_predictive_control wato_trajectory_msgs infrastructure_bringup tf_static_relay vision_msgs_markers wato_lifecycle_manager ackermann_mux camera_calib lidar_lidar_calib can_state_estimator dummy_ackermann eve_description interfacing_bringup joystick_interfacing oscc_interfacing pid_control roscco_msg sensor_interfacing camera_object_detection depth_estimation feature_extraction patchworkpp patchworkpp_vendor perception_bringup perception_utils bytetrack_cpp_vendor track_viz_2d tracking traffic_light_detection carla_bringup carla_common carla_control carla_lifecycle carla_localization carla_msgs carla_perception carla_pygame carla_ros_bridge carla_sample_description carla_scenarios carla_teleop simulation_bringup wato_test behaviortree_ros2_vendor behaviour behaviour_markers behaviour_msgs costmap costmap_markers costmap_msgs lanelet_markers lanelet_msgs lidar_localization localization prediction simple_prediction world_model world_model_markers world_model_msgs world_modeling_bringup

ROS Distro
github

API Docs
Browse Code
Wiki

Package Summary

Version	0.0.0
License	Apache 2.0
Build type	AMENT_CMAKE
Use	RECOMMENDED

Repository Summary

Description	Dockerized ROS2 stack for the WATonomous Autonomous Driving Software Pipeline
Checkout URI	https://github.com/watonomous/wato_monorepo.git
VCS Type	git
VCS Version	main
Last Updated	2026-02-24
Dev Status	UNKNOWN
Released	UNRELEASED
Contributing	Help Wanted (-) Good First Issues (-) Pull Requests to Review (-)

Package Description

Node to estimate vehicle state (steering angle, velocity, odometry) from CAN bus and wheel speed data

Maintainers

WATonomous

Authors

No additional authors.