What problem does it solve? Implementing wheel-encoder odometry involves subtle math errors—wrong counting modes, integration drift, overflow bugs, and miscalibrated wheel parameters—that silently corrupt a robot's pose estimate. This Skill provides the complete formulas and calibration procedures to get it right. ## Core Features & Use Cases - Quadrature Decoding & Kinematics: Covers 1x/2x/4x counting modes, distance-per-count computation, and differential-drive forward kinematics (v, omega from wheel velocities). - Pose Integration Methods: Provides Euler, second-order Runge-Kutta (midpoint), and exact arc integration equations with guidance on when each applies. - ROS 2 Publishing & Calibration: Details nav_msgs/Odometry covariance setup, odom→base_link TF publishing, and step-by-step wheel diameter and wheel separation calibration procedures. - Use Case: You are writing an odometry node for a differential-drive robot and the heading drifts during turns—use the Runge-Kutta midpoint integration and the wheel separation calibration procedure to fix it. ## Quick Start Ask the assistant to implement a ROS 2 odometry node that computes pose from quadrature encoder counts using the encoder-odometry-math formulas.
