Lab 05: TurtleBot3 Simulation with RViz - Camera, LiDAR & Odometry

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Lab 05: TurtleBot3 Simulation with RViz

Camera, LiDAR, and odometry monitoring

Overview

This lab introduces TurtleBot3 robot simulation with comprehensive sensor monitoring using RViz. You'll learn to visualize camera feeds, LiDAR data, and understand odometry concepts for robot localization.

Learning Objectives

Set up TurtleBot3 simulation in Gazebo
Use RViz to monitor camera and LiDAR sensors
Understand odometry data and robot pose estimation
Implement teleoperation control for TurtleBot3
Analyze sensor data for navigation applications

Prerequisites

Software Requirements

ROS 2 Jazzy Jalisco
TurtleBot3 packages (from source)
Gazebo simulation environment
RViz visualization tool

Installation Commands

Install Gazebo and Dependencies:

sudo apt-get update
sudo apt-get install curl lsb-release gnupg
sudo curl https://packages.osrfoundation.org/gazebo.gpg --output /usr/share/keyrings/pkgs-osrf-archive-keyring.gpg
echo "deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/pkgs-osrf-archive-keyring.gpg] http://packages.osrfoundation.org/gazebo/ubuntu-stable $(lsb_release -cs) main" | sudo tee /etc/apt/sources.list.d/gazebo-stable.list > /dev/null
sudo apt-get update
sudo apt-get install gz-harmonic
 
# Install Navigation and SLAM packages
sudo apt install ros-jazzy-cartographer ros-jazzy-cartographer-ros
sudo apt install ros-jazzy-navigation2 ros-jazzy-nav2-bringup
sudo apt install ros-jazzy-rviz2 ros-jazzy-rqt*

Install TurtleBot3 Packages (in your ros_ws):

cd ~/Desktop/ROS-2-Practical-Course-Roadmap-2025/ros_ws/src
git clone -b jazzy https://github.com/ROBOTIS-GIT/DynamixelSDK.git
git clone -b jazzy https://github.com/ROBOTIS-GIT/turtlebot3_msgs.git
git clone -b jazzy https://github.com/ROBOTIS-GIT/turtlebot3.git
git clone -b jazzy https://github.com/ROBOTIS-GIT/turtlebot3_simulations.git
 
# Build the workspace
cd ~/Desktop/ROS-2-Practical-Course-Roadmap-2025/ros_ws
colcon build --symlink-install
source install/setup.bash

Environment Setup:

# Add to ~/.bashrc
echo 'export TURTLEBOT3_MODEL=waffle_pi' >> ~/.bashrc
echo 'export ROS_DOMAIN_ID=30 #TURTLEBOT3' >> ~/.bashrc
source ~/.bashrc

Understanding Key Concepts

What is Odometry?

Odometry is the use of data from motion sensors to estimate change in position over time. In robotics:

Purpose: Track robot's position and orientation (pose) relative to a starting point
Data Sources: Wheel encoders, IMU (Inertial Measurement Unit), visual odometry
Limitations: Accumulates error over time (drift), affected by wheel slip, sensor noise
Applications: Dead reckoning, sensor fusion with other localization methods

Odometry Message Structure

The nav_msgs/msg/Odometry message contains:

# Pose (position + orientation) with uncertainty
geometry_msgs/PoseWithCovariance pose
  geometry_msgs/Pose pose
    geometry_msgs/Point position      # x, y, z coordinates
    geometry_msgs/Quaternion orientation  # rotation as quaternion
  float64[36] covariance             # uncertainty matrix
 
# Velocity with uncertainty
geometry_msgs/TwistWithCovariance twist
  geometry_msgs/Twist twist
    geometry_msgs/Vector3 linear     # linear velocity (x, y, z)
    geometry_msgs/Vector3 angular    # angular velocity (x, y, z)
  float64[36] covariance            # velocity uncertainty

TurtleBot3 Sensor Configuration

Camera: RGB camera for visual perception
LiDAR: 360° laser scanner for obstacle detection and mapping
IMU: Inertial measurement unit for orientation and acceleration
Wheel Encoders: Track wheel rotation for odometry calculation

Enhanced Lab Features

This lab includes a custom TurtleBot3 monitoring package (turtlebot3_lab05) with advanced features:

Custom Monitoring Nodes

Real-time Odometry Monitor (odometry_monitor)
- Live position and velocity tracking
- Commanded vs. actual velocity comparison
- Distance calculations and obstacle detection
- TF transform status monitoring
Sensor Data Logger (sensor_logger)
- Automated data collection from all sensors
- JSON export for analysis
- Statistical summaries of sensor performance

Complete Lab Launch

Quick Start - All-in-One Launch:

cd ~/Desktop/ROS-2-Practical-Course-Roadmap-2025/ros_ws
export TURTLEBOT3_MODEL=waffle_pi
export ROS_DOMAIN_ID=30
source install/setup.bash
ros2 launch turtlebot3_lab05 lab05_complete.launch.py

This launches:

Gazebo simulation with TurtleBot3
RViz with pre-configured sensor displays
Real-time odometry monitoring
Sensor data logging
All required ROS-Gazebo bridges

Control Methods

If keyboard teleop encounters terminal issues, use direct command publishing:

You can use teleopkey node from lap04 but you need to modify the message type
Use this ready to use node

  export TURTLEBOT3_MODEL=waffle_pi
  export ROS_DOMAIN_ID=30
  source install/setup.bash
  ros2 run turtlebot3_teleop teleop_keyboard

Manual Control via Topic Publishing:

# Move forward and turn right
ros2 topic pub --rate 10 /cmd_vel geometry_msgs/msg/TwistStamped \
'{header: {stamp: {sec: 0, nanosec: 0}, frame_id: ""},
  twist: {linear: {x: 0.2, y: 0.0, z: 0.0}, angular: {x: 0.0, y: 0.0, z: 0.1}}}'
 
# Stop the robot
ros2 topic pub --once /cmd_vel geometry_msgs/msg/TwistStamped \
'{header: {stamp: {sec: 0, nanosec: 0}, frame_id: ""},
  twist: {linear: {x: 0.0, y: 0.0, z: 0.0}, angular: {x: 0.0, y: 0.0, z: 0.0}}}'

Note: TurtleBot3 in this lab uses TwistStamped messages on /cmd_vel topic, which includes header information for better timestamp tracking.

RViz Display Configuration

Essential Displays for This Lab

Robot Model
- Shows TurtleBot3 3D model
- Topic: Robot description
LaserScan
- Topic: /scan
- Style: Points or Spheres
- Size: 0.1m
- Color: By intensity or fixed
Image
- Topic: /camera/image_raw
- Transport: compressed (for performance)
Odometry
- Topic: /odom
- Arrow Length: 0.3m
- Show Trail: Yes (last 100 poses)
TF
- Shows coordinate frame relationships
- Enable frames: base_link, odom, map

Saving RViz Configuration

Save your RViz setup for future use:

File → Save Config As → turtlebot3_lab05.rviz

Topic Analysis Commands

# List all active topics
ros2 topic list
 
# Get topic information
ros2 topic info /scan
ros2 topic info /odom
ros2 topic info /camera/image_raw
 
# Check message frequency
ros2 topic hz /scan
ros2 topic hz /odom
 
# View message structure
ros2 interface show sensor_msgs/msg/LaserScan
ros2 interface show nav_msgs/msg/Odometry
ros2 interface show sensor_msgs/msg/Image

Understanding TurtleBot3 TF Tree

The Transform (TF) tree shows the spatial relationships between coordinate frames:

map → odom → base_footprint → base_link → sensors (camera_link, base_scan)

map: Global reference frame
odom: Odometry frame (drifts over time)
base_link: Robot's main body frame
base_scan: LiDAR sensor frame
camera_link: Camera sensor frame

View the TF tree:

ros2 run tf2_tools view_frames

Troubleshooting

Common Issues

TurtleBot3 Model Not Set
```
export TURTLEBOT3_MODEL=waffle_pi
```
Missing TurtleBot3 Gazebo Package
- Error: Package 'turtlebot3_gazebo' not found
- Solution: Ensure turtlebot3_simulations repository is cloned and built:
```
cd src/
git clone -b jazzy https://github.com/ROBOTIS-GIT/turtlebot3_simulations.git
cd .. && colcon build --symlink-install
```
RViz Shows No Robot Model
- Check that robot_state_publisher is running
- Verify URDF is being published
No Camera Image in RViz
- Check camera plugin in Gazebo
- Verify topic name: /camera/image_raw
LiDAR Data Not Visible
- Ensure Fixed Frame is set correctly
- Check LaserScan topic: /scan

Teleop Keyboard Issues

# Alternative: Direct velocity command publishing
ros2 topic pub --rate 10 /cmd_vel geometry_msgs/msg/TwistStamped \
'{header: {stamp: {sec: 0, nanosec: 0}, frame_id: ""},
  twist: {linear: {x: 0.2, y: 0.0, z: 0.0}, angular: {x: 0.0, y: 0.0, z: 0.1}}}'

Robot Not Moving Despite Commands
- Check Gazebo simulation state: Ensure simulation is not paused
- Verify command reception: Use odometry monitor to confirm velocity commands are received
- Check topic connection: Verify /cmd_vel topic has publishers and subscribers
- Gazebo physics: Restart Gazebo if physics engine becomes unresponsive

Useful Debugging Commands

# Check running nodes
ros2 node list
 
# Verify transforms
ros2 run tf2_ros tf2_echo map base_link
 
# Monitor system performance
ros2 node info /gazebo
 
# Check topic connections
ros2 topic info /cmd_vel
ros2 topic info /odom
 
# Verify message flow
ros2 topic echo /cmd_vel --once
ros2 topic echo /odom --once
 
# Test workspace build
./test_lab05.sh

Performance Optimization

Gazebo Performance: If simulation runs slowly, reduce camera quality or disable unused plugins
RViz Performance: Limit point cloud size and reduce update rates for better visualization performance
Network Issues: Ensure ROS_DOMAIN_ID is set consistently across all terminals

Expected Learning Outcomes

After completing this lab, you should understand:

TurtleBot3 Architecture: Robot model, sensors, and coordinate frames
RViz Visualization: How to configure and use RViz for sensor monitoring
Odometry Concepts: Position tracking, uncertainty, and limitations
Sensor Integration: How camera and LiDAR data complement each other
TF Relationships: Coordinate frame transformations in ROS 2

Next Steps

This lab prepares you for:

Lab 06: SLAM (Simultaneous Localization and Mapping)
Lab 07: Autonomous Navigation with Nav2
Advanced sensor fusion and localization techniques