wheel-odometry-model

Name: wheel-odometry-model
Availability: InStock
Author: wimble

Calibrate wheel odometry parameters for differential-drive robots.

18|2|Updated Apr 20, 2026

Search Tags:#ros2 #odometry #robot-localization #covariance #differential-drive #wheel-odometry #encoder-ticks

Ready to run

npx skills add https://github.com/wimblerobotics/ros2-copilot-skills --skill wheel-odometry-model

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: wheel-odometry-model
Source: https://github.com/wimblerobotics/ros2-copilot-skills/tree/main/wheel-odometry-model
Command: npx skills add https://github.com/wimblerobotics/ros2-copilot-skills --skill wheel-odometry-model

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Configures and calibrates wheel odometry models for differential-drive robots, enabling accurate pose estimation by converting encoder ticks to linear and angular motion and managing covariance settings.

Core Features & Use Cases

Kinematics modeling: compute v and omega from wheel data for differential drive.
Encoder-to-distance mapping: convert encoder ticks to linear distance using wheel diameter and ticks-per-rev.
Pose integration & covariance management: update pose and configure covariance for localization.
ROS integration examples: demonstrates a Python node skeleton and robot_localization integration guidance.

Quick Start

Configure wheel_diameter, wheel_separation, and ticks_per_rev, and run the odometry node to publish odometry.

Frequently Asked Questions about wheel-odometry-model

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I calibrate wheel odometry for a differential-drive robot in ROS 2?▼

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.

What is the process for converting encoder ticks to linear distance for robot localization?▼

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.

Can I integrate wheel odometry output with the robot_localization package?▼

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.

Does configuring wheel odometry require setting covariance matrices for differential drives?▼

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.

Why does my differential-drive robot have inaccurate pose estimation despite correct encoder data?▼

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.