Wheel_IMU_EKF

This repository implements Extended Kalman Filter (EKF) to fuse wheel odometry and IMU data for robust motion estimation.

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Features

• Real-time fusion of wheel odometry and IMU data.
• Noise filtering and state estimation with an EKF.
• Designed for applications in robotics and autonomous vehicles.

Data Source

The dataset and reference implementation are inspired by:

@misc{ztd2021viwo,
  title={VIW-Fusion: Visual-Inertial-Wheel Fusion Odometry},
  author={Tingda Zhuang},
  howpublished={\url{https://github.com/TouchDeeper/VIW-Fusion}},
  year={2021}
}

Requirements

To use this repository, ensure you have the following dependencies installed:

• ROS (Robot Operating System) - For data handling and communication.
• Eigen3 - Linear algebra library.
• C++11 or higher - Required for modern C++ features.

Installation

1. Clone this repository:

   git clone https://github.com/lichengyang-robot/wheel_imu_ekf.git
   cd wheel_imu_ekf

2. Build the workspace:

   mkdir -p ~/catkin_ws/src
   mv wheel_imu_ekf ~/catkin_ws/src/
   cd ~/catkin_ws
   catkin_make
   source devel/setup.bash

3. Run the nodes:

   rosrun imu_wheel_localization eskf_node

Contribution

Contributions are welcome! If you have any suggestions or improvements, feel free to submit a pull request or open an issue.