ekf-sensor-fusion

Name: ekf-sensor-fusion
Availability: InStock
Author: wimble

Configure robot_localization's ekf_filter_node to fuse odometry, IMU, pose, and twist sources.

18|2|Updated Apr 20, 2026

Search Tags:#localization #ros2 #sensor-fusion #odometry #imu #ekf #robot_localization

Ready to run

npx skills add https://github.com/wimblerobotics/ros2-copilot-skills --skill ekf-sensor-fusion

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Configure robot_localization EKF to fuse multiple odometry, IMU, pose, and twist sources into a single, smooth state estimate for ROS 2 robotics development.

Core Features & Use Cases

15-state EKF with configurable inputs: supports odom0, imu0, pose0, twist0 sources and corresponding fuse masks.
2D-mode ready: provides two_d_mode configuration to constrain Z, roll, and pitch for ground robots.
Frame and timing guidance: includes map/odom/base_link framing options, sensor timeout, and transform handling for reliable localization.

Quick Start

Load this EKF configuration into robot_localization and start the ekf_filter_node with the configured topics.

Frequently Asked Questions about ekf-sensor-fusion

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

FAQPage Schema

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

Sensor fusion for ROS 2 localization combines odometry, IMU, and pose data into a single smooth state estimate by configuring a 15-dimensional EKF with explicit frames, covariances, and timing parameters.

What is the best way to configure robot_localization ekf_filter_node with multiple sensors?▼

Configuring the robot_localization ekf_filter_node involves setting odom0, imu0, pose0, and twist0 inputs alongside corresponding fuse masks to properly integrate multiple sensor sources into one accurate estimate.

Can I use a 2D mode EKF configuration for ground robot sensor fusion?▼

Yes, 2D mode constrains Z, roll, and pitch dimensions during sensor fusion, making it directly applicable for ground robots by limiting the 15-dimensional state estimate to planar navigation.

How do I set up map, odom, and base_link frames for ROS 2 EKF?▼

Setting up frames for ROS 2 EKF requires configuring map, odom, and base_link framing options alongside sensor timeout and transform handling to ensure reliable mobile robot localization.

Why does my robot_localization EKF output jump when fusing GPS and odometry?▼

Jumpy EKF output during GPS and odometry fusion often stems from improperly configured sensor covariances, timing parameters, or mismatched frame transforms within the robot_localization state estimation pipeline.