Multi Sensor Fusion (MSF) Localization

Multi Sensor Fusion Localization using Kalman Filters

A modular C++/ROS 2 localization framework for estimating robot/vehicle state by combining measurements from multiple sensors using Kalman-filter-based state estimation.

The project is designed with a separation between the localization/estimation core and the ROS 2 interface, making the estimation algorithms reusable outside of ROS 2.

Modern autonomous vehicles and mobile robots typically rely on several complementary sensors for localization.

For example:

• IMU provides high-rate motion information but accumulates drift.
• GNSS provides globally referenced position but may be noisy or temporarily unavailable.
• Wheel odometry provides high-rate relative motion information from wheel encoders. It is particularly useful for estimating vehicle velocity and short-term motion, but can accumulate error due to wheel slip, uneven terrain, encoder noise, and inaccurate wheel parameters.
• LiDAR odometry provides relative motion information but is affected by environmental conditions and accumulated drift.
• Visual odometry provides motion estimates from camera observations but can fail in challenging visual environments.

Multi-sensor fusion combines these measurements into a single state estimate that is generally more robust than relying on an individual sensor.

This project provides a framework for implementing and experimenting with such localization algorithms using Kalman filtering and state estimation techniques.

The project is divided into two main components:

msf_localization/
├── msf_localization_core/
│   ├── apps/
│   ├── include/
│   │   └── filters/
│   ├── src/
│   │   └── filters/
│   └── CMakeLists.txt
│
├── msf_localization_ros2/
│   ├── config/
│   ├── include/
│   │   └── msf_localization_ros2/
│   ├── launch/
│   ├── rviz/
│   ├── src/
│   ├── CMakeLists.txt
│   └── package.xml
│
├── LICENSE
└── README.md

The repository currently contains a dedicated msf_localization_core package for the estimation algorithms and an msf_localization_ros2 package containing the ROS 2 integration, launch files, configuration and RViz resources.

The core library contains the localization and filtering logic.

Its purpose is to remain independent from ROS-specific message types and communication mechanisms wherever possible.

This makes it possible to use the estimation algorithms in:

• ROS 2 nodes
• standalone C++ applications
• simulation environments
• offline dataset processing
• unit tests and research experiments

The ROS 2 package provides the interface between the estimation core and the ROS ecosystem.

It contains:

• ROS 2 node implementation
• configuration files
• launch files
• RViz configuration
• ROS 2 publishers/subscribers

Kitti 2011_09_26_0014 result for LKF

Kitti 2011_10_03_0042 result for LKF

• ROS2 Humble
• Eigen
• GeographicLib

Clone the repository:

git clone -b humble-dev https://github.com/aliaydinkucukcollu/msf_localization.git
cd msf_localization

Source ROS 2 Humble:

source /opt/ros/humble/setup.bash

Build the workspace:

colcon build --cmake-args -DCMAKE_EXPORT_COMPILE_COMMANDS=ON

Launch the ROS 2 localization node with:

source /opt/ros/humble/setup.bash
source install/setup.bash

ros2 launch msf_localization_ros2 msf_localization_ros2.launch.py

The ROS 2 package contains dedicated directories for configuration, launch files and RViz visualization.

The project is being developed around several goals.

Filtering algorithms should be independent of the ROS 2 interface.

                ┌────────────────────┐
                │ Localization Core  │
                │                    │
                │  Kalman Filters    │
                │  State Models      │
                │  Measurement Models│
                └─────────▲──────────┘
                          │
                          │
                ┌─────────┴──────────┐
                │      ROS 2         │
                │                    │
                │ Subscribers        │
                │ Publishers         │
                │ Parameters         │
                │ Launch             │
                └────────────────────┘

The estimator should be capable of incorporating measurements from different sensor sources without tightly coupling the filtering implementation to a particular sensor driver.

The project is intended to make it straightforward to experiment with different:

• State representations
• Motion models
• Measurement models
• Kalman-filter variants
• Noise models
• Sensor combinations

The framework is particularly suitable for robotics and autonomous-vehicle localization research where GNSS, IMU and relative odometry sources need to be combined.

This project is licensed under the MIT License.

See LICENSE for details.

Ali Aydın Küçükçöllü

This project is developed as an educational and research-oriented framework for understanding and implementing multi-sensor state estimation techniques for robotics and autonomous vehicles.

• Probabilistic Robotics Sebastian Thrun, Wolfram Burgard, Dieter Fox. MIT Press, 2005.

• State Estimation for Robotics Timothy D. Barfoot. Cambridge University Press, 2017.

If you find this project useful, consider giving it a ⭐ on GitHub.