ROS 2 C++ SLAM Turtlebot3 RViz TF LiDAR
Real-world deployment of the EKF-SLAM algorithm with landmark detection & association, run onboard the Turtlebot3 with real-time visualization in RViz. Key: Green/multicolored dots: LIDAR scan points. Green obstacles+robot+path = SLAM estimate. Blue robot+path = odometry-only estimate. White obstacles=landmarks detected by the `landmarks` node (see below), which feed into SLAM. SLAM's robot pose covariance is shown as the purple ellipse and yellow cone.

SLAM from Scratch: ROS 2 Navigation Stack for Turtlebot3

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Authors: Conor Hayes

In this project, I implement a full ROS 2 navigation stack for the Turtlebot3 Burger differential drive mobile robot, including LiDAR SLAM, control, and simulation components, all built from scratch in C++. This means no external SLAM or kinematics libraries, no Gazebo simulation, just basic ROS 2 packages, armadillo for linear algebra, and my own implementations of the necessary algorithms and tools.

Highlights

  • EKF (Extended Kalman Filter) SLAM implementation using LiDAR data + odometry for mapping and localization
  • Custom data association + lidar clustering & classification based on circle fitting for landmark extraction
  • Custom forward + inverse kinematics implementation (turtlelib) for differential drive robot
  • Custom simulator (nusim) with configurable arena and obstacles
  • Visualization + tooling in RViz for real-time robot state display in both simulation and hardware
  • Deployment to real hardware (Turtlebot3 Burger) for testing in a physical environment
Custom-built LiDAR SLAM simulator, with ground truth (red), odometry-only (blue), and SLAM (green) map & pose estimates shown, as well as obstacle detection in white.

Tech Stack

  • ROS 2 Kilted Kaiju
  • C++23
  • Turtlebot3 Burger

Packages

  • turtle_control: command and odometry pipeline for Turtlebot3
  • nuturtle_description: URDF and RViz assets for multiple robot instances
  • turtlelib: SE(2), diff-drive kinematics, and SVG helpers
  • nusim: RViz-based simulator for SLAM development

Acknowledgements

Big thanks to Dr. Matthew Elwin, who runs the MSR program at Northwestern, and teaches the course in which this project was developed (ME495: Sensing, Navigation, and Machine Learning).

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