What problem does it solve? Computing accurate wheel odometry for a differential drive robot requires correct kinematics, careful calibration of wheel parameters, and properly tuned covariance matrices—mistakes in any of these cause drift, arcs instead of straight lines, and poor sensor fusion results. ## Core Features & Use Cases - Differential Drive Kinematics: Derives linear and angular velocity from left/right wheel encoder ticks using wheel separation and diameter. - Pose Integration: Integrates velocities into x, y, yaw pose using the midpoint method for better accuracy on curved paths. - ROS2 Odometry Publishing: Provides a complete Python node skeleton publishing nav_msgs/Odometry with pose/twist covariance and optional TF broadcast. - robot_localization Integration: Shows EKF configuration for fusing wheel odometry with IMU data. - Use Case: When your robot drifts in arcs or reports wrong distances, use this Skill to identify whether wheel_separation, wheel_diameter, or ticks_per_rev is miscalibrated and how to fix it. ## Quick Start Ask the AI to generate a ROS2 differential drive odometry node with calibrated wheel parameters and covariance settings for your robot.
