i2Nav-WHU/Wheel-SLAM

Wheel-SLAM originates from the i2Nav-WHU lab (Wuhan University), a respected institution in positioning and navigation. The project's primary moat is the specific mathematical formulation required to handle an IMU mounted on a rotating wheel (where centripetal acceleration and gravity signals are complex) rather than the chassis. With 148 stars and 21 forks, it has respectable academic traction but lacks the 'infrastructure' status of larger SLAM frameworks like ORB-SLAM3 or LIO-SAM. The velocity is 0.0, indicating this is a static research artifact associated with a publication rather than an evolving software product. Frontier labs (OpenAI/Google) are focused on end-to-end vision-language-action models and are unlikely to compete in low-level wheel kinematics. However, the project's defensibility is limited by its niche application; most commercial robotics companies prefer multi-sensor fusion (Visual-Inertial or LiDAR-Inertial) on the chassis. The 'moat' is essentially the domain-specific knowledge of wheel-mounted IMU error models, which is easily replicated by specialized robotics teams if the use case (extremely low-cost or high-slippage terrain) justifies it.