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+.. _roscon-uk-2025-state-estimation:
+
+ROSCon UK 2025: State Estimation for Mobile Robotics Workshop
+*************************************************************
+
+- `Overview`_
+- `Workshop Content`_
+
+Overview
+========
+
+This tutorial provides comprehensive hands-on experience with state estimation techniques for mobile robotics, presented as a workshop at ROSCon UK 2025.
+State estimation is a fundamental component of any autonomous mobile robot system, enabling robots to maintain accurate knowledge of their position, orientation, and velocity in the world.
+This workshop covers two complementary approaches to state estimation that are widely used in production robotics systems.
+
+The workshop materials explore both classical filtering approaches and modern optimization-based methods for sensor fusion.
+Whether you're building a warehouse robot, an outdoor autonomous vehicle, or any other mobile platform, understanding these state estimation techniques is crucial for achieving reliable navigation and localization performance.
+The hands-on exercises use real ROS 2 packages that are production-tested and widely deployed in commercial robotics applications.
+
+The complete workshop materials, including setup instructions, exercises, and solutions, are available in the official repository:
+
+`ROSCon UK 2025 State Estimation Workshop Repository <https://github.com/locusrobotics/roscon-uk-2025-se-workshop>`_
+
+Workshop Content
+================
+
+The workshop covers two powerful state estimation frameworks:
+
+**Robot Localization - Kalman Filter-Based Sensor Fusion**
+
+Robot Localization is a ROS package that provides Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) implementations for fusing data from multiple sensors.
+This approach is particularly well-suited for real-time state estimation where computational efficiency is critical.
+The workshop demonstrates how to configure Robot Localization to fuse IMU data, wheel odometry, GPS, and other sensors to produce smooth, accurate state estimates.
+Topics include:
+
+- Understanding the Extended Kalman Filter and Unscented Kalman Filter algorithms
+- Configuring sensor inputs and fusion parameters
+- Tuning process and measurement noise covariances
+- Setting up coordinate frame transforms for proper sensor fusion
+- Handling different sensor update rates and characteristics
+- Debugging common state estimation issues
+
+**Fuse - Factor Graph Optimization**
+
+Fuse is a general architecture for performing sensor fusion using factor graphs.
+Unlike traditional filtering approaches, factor graph optimization considers a history of measurements to jointly optimize the robot's trajectory.
+This batch optimization approach can produce more accurate results by reasoning about past measurements and future constraints simultaneously.
+The workshop covers:
+
+- Introduction to factor graph concepts and optimization
+- Building factor graphs for mobile robot state estimation
+- Integrating various sensor modalities into the factor graph framework
+- Understanding the trade-offs between filtering and optimization approaches
+- Fixed-lag smoothing for real-time performance
+- Loop closure detection and handling with factor graphs
+
+Both approaches have their strengths.
+The workshop provides practical experience with both methods, enabling you to choose the right approach for your specific robotics application.
+
+Check out the full workshop materials in the repository: https://github.com/locusrobotics/roscon-uk-2025-se-workshop
diff --git a/tutorials/index.rst b/tutorials/index.rst
index d440fcd695..eed558906b 100644
--- a/tutorials/index.rst
+++ b/tutorials/index.rst
@@ -29,3 +29,4 @@ Nav2 Tutorials
    docs/docker_dev.rst
    docs/route_server_tools.rst
    docs/navigation2_with_vector_objects.rst
+   docs/roscon_uk_2025_state_estimation.rst