The SLAM Maze Navigator
Simultaneous Localization and Mapping under odometry drift uncertainty.
What is The SLAM Maze Navigator?
SLAM (Simultaneous Localization and Mapping) is the foundational algorithmic problem in autonomous robotics where an agent must build a consistent spatial map of an unknown environment while concurrently tracking its own precise geometric location within that newly constructed map using noisy onboard sensors.
How the Interactive Simulation Works
Drive a simulated rover through an unknown maze while its particle filter simultaneously maps walls and computes self-localization.
Mathematical Formulation & Computational Complexity
The simulation dynamically resolves the state transition function given by:
Extended Kalman Filter SLAM scales at $\mathcal{O}(M^2)$ per update where $M$ is map landmarks, while modern graph-based pose SLAM operates at $\mathcal{O}(P^3)$ during bundle adjustment optimization of pose nodes $P$. Sensor noise sliders demonstrate how odometry drift accumulates without loop-closure factor constraints.
Industrial AI & Real-World Engineering Applications
Navigating subterranean Martian lava tubes and lunar craters completely denied of external GPS signals.
Simultaneous floor mapping and sanitization path tracking in dynamic clinical hallways.
Tracking headset 6-DoF spatial poses and anchoring virtual holograms to physical tables and walls.