When mapping with LiDAR and SLAM, you might notice some inconsistencies—walls wobble, or the robot drifts slightly. This isn’t a problem with your LiDAR; it’s a sign that your robot needs sensor fusion. By combining data from sensors like encoders, IMUs, and LiDAR, ROS2's EKF (Extended Kalman Filter) and AMCL (Adaptive Monte Carlo Localization) work together to stabilize your robot’s localization.
Why Sensor Fusion Matters
Every robot needs to answer the critical question: "Where am I?" Sensors like encoders, IMUs, and LiDAR each provide valuable data but come with weaknesses—wheel slippage, gyro drift, and noisy scans. Sensor fusion blends these signals, creating more accurate and reliable robot positioning.
Getting Started with Sensor Fusion
To implement sensor fusion in ROS2, install the robot_localization package:
bash
sudo apt install ros-humble-robot-localization
For configuration, use this sample ekf.yaml to blend encoder and IMU data:
yaml
ekf_filter_node:
ros__parameters:
frequency: 50
sensor_timeout: 0.1
two_d_mode: true
publish_tf: true
map_frame: map
odom_frame: odom
base_link_frame: base_link
odometry0: /wheel_odom
imu0: /imu/data
Launch with:
bash
ros2 run robot_localization ekf_node --ros-args --params-file ekf.yaml
Adding AMCL for Better Localization
While EKF handles sensor fusion, AMCL helps the robot align with a pre-built map using LiDAR scans. Use this to launch AMCL:
bash
ros2 launch nav2_bringup localization_launch.py map:=/path/to/map.yaml
AMCL refines your robot’s pose using real-time data from LiDAR, keeping your robot accurately positioned within the map.
Tuning and Testing
Tuning both EKF and AMCL parameters is key to improving performance. For EKF, adjust the frequency and sensor_timeout values, and for AMCL, tweak laser_max_beams and min_particles.
The Real Power of Sensor Fusion
To test your setup, drive the robot manually. You'll notice that IMU and encoder data alone lead to drift, but when you enable sensor fusion with EKF and AMCL, the robot’s pose stabilizes and remains accurate.
Want to Learn More?
Now that you have stable localization, it’s time to move on to path planning and obstacle avoidance. If you’re interested in mastering ROS2 for mobile robots, check out our Mobile Robotics Engineering Course at Robotisim. Learn the skills to create smart, autonomous robots today!
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