ROS2 EKF Sensor Fusion: IMU + Wheel Odometry

Hard State Estimation 57% pass rate
#python#kalman-filter#sensor-fusion#ros2

Practice the ROS2 EKF Sensor Fusion: IMU + Wheel Odometry coding problem in State Estimation. Browser-based execution with automated grading — no local ROS install required. 57% of engineers pass this challenge.

Problem Statement

Implement a ROS2 node that performs sensor fusion using an Extended Kalman Filter (EKF). You will fuse IMU angular velocity and Wheel Encoder linear velocity into a filtered 2D pose estimate.

Requirements:

  • Node name: ekf_fusion_node
  • Subscribe to:
    • /imu/data_raw (sensor_msgs/Imu) -> Provides angular velocity
    • /odom_raw (nav_msgs/Odometry) -> Provides linear velocity
  • Publish to:
    • /ekf_pose (geometry_msgs/PoseStamped) -> Filtered Pose
  • State Vector: [x, y, theta]

Extended Details:

  • Prediction Step: Triggered by incoming Odom or Imu (or a timer). Use a constant velocity model.
  • Update Step: Triggered by Odom (measuring linear velocity) and Imu (measuring orientation/yaw rate).

Input/Output Format

Input: /imu/data_raw, /odom_raw | Output: /ekf_pose (PoseStamped)

⚠️ Common Pitfalls

  • Forgetting to handle angle wrapping (keep theta within -pi to pi).
  • Using uninitialized covariance matrices (P).
  • Incorrect Jacobian implementation for the non-linear motion model.

📚 Helpful Resources

Frequently asked questions

What is the ROS2 EKF Sensor Fusion: IMU + Wheel Odometry practice problem?

It is a hands-on State Estimation challenge on SimuCode where you implement and run ROS2 code in the browser with runtime-verified tests.

How do I practice ROS2 EKF Sensor Fusion: IMU + Wheel Odometry online?

Open this page, sign in, and solve the problem in the built-in IDE. Your solution is graded against real ROS2 execution checks.

What skills does ROS2 EKF Sensor Fusion: IMU + Wheel Odometry test?

This Hard problem focuses on python, kalman-filter, sensor-fusion skills used in robotics interviews and production systems.