Student builds taorobot: complete from-scratch mobile robot stack for learning
One student writes every robot algorithm himself to truly understand robotics.
Jin Tao, a student passionate about robotics, open-sourced taorobot — a mobile robot software stack written entirely from scratch for ROS 2 Humble and Gazebo. Frustrated with the complexity of frameworks like Nav2 and SLAM Toolbox, which bury algorithms under layers of abstraction, Tao decided to implement every component himself as a learning project. The result is a minimalist system where each algorithm lives in its own plain ROS 2 node with minimal dependencies, making it ideal for students who want to understand how robot autonomy really works.
The stack covers the full pipeline: occupancy-grid mapping, particle-filter localization, graph-pose SLAM (using correlative scan matching and g2o for loop closure), FastSLAM, A* global planning with spline smoothing, and pure-pursuit control. It's validated on a single simulated robot in one world and includes three demo scenarios with realistic noisy odometry. Each package has a README that explains the design rationale, and YAML configs describe why each parameter has its value — not just what it does. The author emphasizes it is not a Nav2 replacement; there are no recovery behaviors and likely errors remain. Tao welcomes corrections and sees this as the code he wishes he had when he started learning robotics.
- Implements occupancy-grid mapping, particle-filter localization, graph-pose SLAM with g2o, FastSLAM, A* planning, and pure-pursuit control in C++.
- Each algorithm is a single ROS 2 node with minimal dependencies and explanatory READMEs for learning.
- Currently validated only on one simulated robot; lacks recovery behaviors — intended as an educational resource.
Why It Matters
Gives students transparent, self-contained robot code to learn fundamentals without black-box tools.