ROS 2 robot description, Gazebo simulation for oomwoo-one, the first OOMWOO open-source robot vacuum model. TODO download 3D printing STEP/3MF and CAD design.

Tutorials:

• Simulate oomwoo-one in Gazebo with ROS 2
• Write your first oomwoo ROS 2 package

Video: OOMWOO One Step-by-step Gazebo/ROS2 simulation tutorial

Package contents

• urdf/ — xacro description of the ~349 mm round vacuum (body + LiDAR turret, diff-drive wheels, caster). Frames follow the Kaia.ai convention: base_footprint → base_link → base_scan.
• config/ekf.yaml — robot_localization EKF that fuses /odom + /imu and publishes the odom → base_footprint transform (the bridge publishes the /odom topic but not this TF, which cartographer requires).
• config/cartographer_lds_2d.lua, config/navigation.yaml, … — SLAM / Nav2 tuning.
• config/gz_bridge.yaml, urdf/plugins.xacro — Gazebo simulation: diff-drive, odometry (ground-truth + wheel), 2D LiDAR, side distance sensors, a front multizone ToF, front stereo cameras, and front bumper contact sensors. See Simulation: sensors, topics & tuning below, and docs/sim-bumpers.md for how the simulated bumpers are wired and the three gz-sim gotchas that make them easy to break.
• launch/bringup.launch.py — physical bring-up: bridge + robot_state_publisher + EKF.

Usage

Select the robot model (used by the shared Kaia.ai launch files):

kaia config robot.model oomwoo_one

Simulation (no robot needed)

ros2 launch oomwoo_gazebo world.launch.py
ros2 launch oomwoo_bringup navigation.launch.py use_sim_time:=true slam:=True
ros2 run kaiaai_teleop teleop_keyboard

Physical robot

The robot must be on the LAN running SangamIO (see the Proscenic root & setup tutorial for flashing/Wi-Fi).

ros2 launch oomwoo_one bringup.launch.py robot_ip:=<robot-ip>
ros2 launch oomwoo_bringup navigation.launch.py slam:=True
ros2 run kaiaai_teleop teleop_keyboard
ros2 run nav2_map_server map_saver_cli -f ~/maps/map

You can store the robot IP once instead of passing robot_ip:= every time:

kaia config robot.ip <robot-ip>
ros2 launch oomwoo_one bringup.launch.py

(Precedence: an explicit robot_ip:= wins, otherwise kaia config robot.ip, otherwise 192.168.1.143.)

Simulation: sensors, topics & tuning

Everything below is for the Gazebo sim (ros2 launch oomwoo_gazebo world.launch.py). The gz sensors are defined in urdf/plugins.xacro and bridged to ROS 2 by config/gz_bridge.yaml; all sizes/optics are tunable params in urdf/params.xacro.

Sensors

Sensor	ROS topic(s)	Type	Details	Frame
2D LiDAR (turret)	/scan	sensor_msgs/LaserScan	360 samples, 5 Hz, 0.1–10 m	base_scan
Side distance L / R	/range_left, /range_right	sensor_msgs/LaserScan	short-range wall sensors aimed straight out (±90°), ~0.02–0.5 m; a real ToF (sensor_msgs/Range) on hardware	range_left_link, range_right_link
Front multizone ToF	/tof_front/points	sensor_msgs/PointCloud2	16×8 depth grid, 120°H × 60°V, 0.02–4 m — models two VL53L7CX (each 8×8, 60°) at ±30°	tof_front_link
Front stereo cameras L / R	/camera_left/image, /camera_right/image (+ …/camera_info)	sensor_msgs/Image, `CameraInfo$	\text{RGB}, \text{VGA} 640 \times 480, 120° \text{HFoV}, ~50 \text{mm} \text{base} (\text{OV5647}-\text{equivalent})	$camera_{left,right}_optical_frame`
Front bumpers L / R	/bumper_left/contact, /bumper_right/contact	ros_gz_interfaces/Contacts	front 180° contact arc; non-empty contacts = pressed	base_link
IMU (gyro + accel)	/imu	sensor_msgs/Imu	6-axis, 100 Hz, near body center; angular velocity + linear acceleration + orientation (ground-truth attitude; the hardware IMU has none). EKF IMU fusion is off for now	imu_link

Rendered sensors (LiDAR, side ranges, ToF, cameras) need the sim's GPU render path. On a headless/no-GPU setup they advertise but read empty (inf/black) — run with a working GL stack (or headless:=true uses software GL, which is slow and may not render depth).

Odometry, TF & actuation

Topic	Type	Notes
/odom	nav_msgs/Odometry	canonical odometry; odom_source picks the stream (below)
/odom_truth, /odom_wheel	nav_msgs/Odometry	the other stream, always published for wheel-slip comparison
/tf, /tf_static	tf2_msgs/TFMessage	odom → base_footprint (from the selected odom source) + the fixed sensor frames
/joint_states	sensor_msgs/JointState	wheel joints
/clock	rosgraph_msgs/Clock	sim time (use use_sim_time:=true)
/cmd_vel	geometry_msgs/Twist	input — velocity command to the diff-drive

Odometry source switch — world.launch.py odom_source:=truth|wheel selects which odom owns /odom + /tf; both streams always publish so you can diff them to measure slip:

odom_source	/odom + /tf	wheel odom on	ground-truth odom on
truth (default)	ground-truth model pose (slip-free)	/odom_wheel	/odom
wheel	wheel-encoder odom (slip drifts)	/odom	/odom_truth

Viewing the sensors

ros2 topic list                              # everything available
ros2 topic hz /scan                          # confirm a sensor is publishing
ros2 topic echo /bumper_left/contact         # bumpers (non-empty contacts = pressed)
ros2 topic echo /range_right                 # side distance
ros2 run rqt_image_view rqt_image_view       # pick /camera_left/image or /camera_right/image
ros2 run tf2_tools view_frames               # dump the TF tree to frames.pdf
# RViz with the shipped config (LiDAR, ToF PointCloud2, cameras, TF):
ros2 launch oomwoo_bringup monitor_robot.launch.py use_sim_time:=true
#   or: rviz2 -d "$(ros2 pkg prefix oomwoo_one)/share/oomwoo_one/rviz/gazebo.rviz"

In RViz, add a LaserScan on /scan (and /range_*), a PointCloud2 on /tof_front/points, Image displays on /camera_*/image, and set the fixed frame to odom (or map once localized).

URDF parameters (urdf/params.xacro)

All dimensions, sensor placements and optics are xacro properties — edit and re-launch. Grouped as:

Group	Example params
Body & drivetrain	base_diameter, wheel_diameter, wheel_base, lower_cylinder_height, caster_*
Front bumper	bumper_facets_per_side, bumper_thickness, bumper_height, bumper_z
Side distance sensors	range_sensor_{min,max,angle_deg,samples,fov_deg,update_rate,z}
Front ToF	tof_front_{h_samples,v_samples,hfov_deg,vfov_deg,min,max,update_rate}
Stereo cameras	camera_{width,height,hfov_deg,baseline,near,far,update_rate}
IMU	imu_{x,y,z,update_rate,gyro_noise,accel_noise}

Related files: urdf/robot.urdf.xacro (links/joints), urdf/plugins.xacro (gz plugins + sensors), urdf/inertial.xacro (mass/inertia macros), urdf/materials.xacro (colors).

world.launch.py arguments (package oomwoo_gazebo)

ros2 launch oomwoo_gazebo world.launch.py <arg>:=<value> …

Argument	Default	Values	Meaning
robot_model	(empty → kaia config robot.model)	package name	robot description package to spawn
world	living_room.world	file in oomwoo_gazebo/worlds	Gazebo world
x_pose, y_pose	-2.0, -0.5	meters	spawn position
use_sim_time	true	true/false	use the sim /clock
headless	false	true/false	server-only, offscreen, software GL (no GUI)
odom_source	truth	truth/wheel	which odometry owns /odom + /tf (see above)

Frame tree

map → odom → base_footprint → base_link → base_scan            (2D LiDAR)
                                        → wheel_left_link / wheel_right_link / caster_link
                                        → range_left_link / range_right_link
                                        → tof_front_link
                                        → camera_{left,right}_link → camera_{left,right}_optical_frame

map → odom comes from AMCL (localization); odom → base_footprint from the selected odom source; the rest are fixed frames from robot_state_publisher.

Notes

• URDF dimensions are approximate (~349 mm diameter, ~95 mm height, 0.233 m wheel base to match the bridge's odometry). Refine against measurements of your robot.
• Vacuum-specific actuators (vacuum/brushes/water pump, LEDs) are exposed by the bridge via /set_actuator, /set_led, /set_lidar and the /actuator_cmd, /led_cmd topics.

License

Apache 2.0