imu-integration

Name: imu-integration
Availability: InStock
Author: wimble

Configure EKF/UKF fusion and gravity removal for IMU data in ROS 2 navigation stacks.

18|2|Updated Apr 20, 2026

Search Tags:#ros2 #sensor-fusion #robot-navigation #odometry #imu #ekf #ukf

Ready to run

npx skills add https://github.com/wimblerobotics/ros2-copilot-skills --skill imu-integration

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Fusion of IMU data to support robust localization and orientation estimation for ROS 2 navigation systems.

Core Features & Use Cases

Orientation estimation using Madgwick or complementary filters
Gravity removal and frame alignment per REP 103
EKF/UKF fusion configuration for 2D and 3D robots
Calibration guidance and static-transform tips for mounting inaccuracies
Real-world use: fuse /imu/data with odometry for odometry-accurate nav

Quick Start

Configure and run the IMU integration workflow to fuse raw IMU data into a coherent orientation estimate for navigation.

Frequently Asked Questions about imu-integration

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

FAQPage Schema

How do I fuse IMU data with odometry for robot localization in ROS 2?▼

IMU data fusion for robot localization in ROS 2 involves configuring EKF or UKF nodes to combine /imu/data with odometry. This workflow aligns sensor frames per REP 103 and removes gravity to provide odometry-accurate navigation.

What is the best way to remove gravity and align IMU frames for sensor fusion?▼

Gravity removal and frame alignment for IMU sensor fusion require configuring static transforms and applying REP 103 standards. This ensures acceleration data accurately reflects robot movement rather than gravitational bias during EKF or UKF processing.

How do I configure EKF and UKF for 3D robot navigation using IMU data?▼

Configuring EKF and UKF for 3D robot navigation using IMU data involves setting orientation, yaw rate, and acceleration handling parameters. Proper calibration and static-transform tuning correct mounting inaccuracies for reliable localization.

Can I use Madgwick or complementary filters for IMU orientation estimation in a ROS 2 navigation stack?▼

Madgwick and complementary filters can be used for IMU orientation estimation within a ROS 2 navigation stack. They process raw IMU data into a coherent orientation estimate before feeding it into EKF or UKF fusion workflows.

Why does my IMU sensor fusion drift or produce inaccurate yaw estimates in ROS 2?▼

IMU sensor fusion drift and inaccurate yaw estimates in ROS 2 often stem from uncalibrated sensors or incorrect static transforms. Proper calibration, mounting alignment, and yaw rate handling configuration are essential to resolve these localization issues.

Do I need to publish static transforms to correct IMU mounting offset for EKF fusion?▼

Publishing static transforms is required to correct IMU mounting offset for EKF fusion. Accurate static-transform configuration aligns the IMU frame with the robot base frame, preventing orientation and localization errors during sensor fusion.