encoder-odometry-math

Compute odometry from quadrature encoder counts by applying forward kinematics to estimate velocity and integrate pose for differential-drive robots. This process provides continuous pose and velocity estimates necessary for navigation and control.

Publish nav_msgs/Odometry and base_link transforms in ROS 2 by running an encoder-based odometry node at 50-100 Hz. This node subscribes to wheel counts, applies pose integration, and outputs reliable odometry data for navigation pipelines.

Covariance estimation for ROS 2 odometry messages models the uncertainty of velocity and pose integration calculations. This covariance modeling is required to produce reliable nav_msgs/Odometry data that accurately reflects sensor noise.

Euler, Runge-Kutta, and exact arc are pose integration methods for estimating robot position from encoder signals. Euler is simplest, while Runge-Kutta and exact arc offer improved accuracy for differential-drive kinematics during turning.

Yes, quadrature encoder signals can be used for velocity estimation in calibration workflows. The computed velocities and forward kinematics are useful for calibrating differential-drive robots and validating simulation models.