wheel-odometry-model

To calibrate wheel odometry for a differential-drive robot in ROS 2, you configure the wheel diameter, wheel separation, and ticks-per-rev parameters. This translates encoder ticks into accurate linear and angular motion for pose estimation.

Converting encoder ticks to linear distance uses the configured wheel diameter and ticks-per-rev values. This encoder-to-distance mapping computes the linear velocity needed to update the robot's pose and manage localization covariance.

Yes, you can integrate wheel odometry with the robot_localization package. The calibration process provides covariance-consistent odometry data and includes a Python node skeleton demonstrating ROS integration for robot_localization workflows.

Yes, configuring wheel odometry requires setting covariance matrices for differential drives. The model manages pose integration and configures covariance settings to ensure the output odometry data is accurate and suitable for localization tools.

Inaccurate pose estimation with correct encoder data often stems from uncalibrated wheel odometry parameters. Incorrect wheel diameter, wheel separation, or ticks-per-rev settings cause errors in computing linear and angular motion from the encoder ticks.