aslyansky-m/FSTD_SLAM

FSTD_SLAM is a legacy student competition project from the Technion Formula Student team. While it represents a functional SLAM implementation for a specific niche (autonomous racing), its defensibility is near-zero for several reasons: it is over seven years old with zero current development velocity, and it likely relies on classical EKF (Extended Kalman Filter) or graph-based SLAM techniques that have since been superseded by more robust LiDAR-inertial odometry (LIO) frameworks like Fast-LIO or LIO-SAM. The project's 62 stars indicate it served as a reference for other student teams, but it lacks the scale, support, or algorithmic edge to compete with professional or modern open-source robotics stacks. Frontier labs (OpenAI, Google) pose no direct risk as the domain is too niche, but the project is functionally displaced by modern robotics frameworks and the ongoing evolution of autonomous racing software (e.g., stacks from teams like AMZ Driverless or commercial entities like Waymore/Applied Intuition). It remains a useful historical reference for the Formula Student community but has no commercial moat.