Accurate State Estimation of Legged Robots using Hybrid Kalman Filtering and Transformer-based Vision

A hybrid approach combining Kalman filtering, optimization, and learning-based methods is proposed to accurately estimate the state of a legged robot's trunk by integrating proprioceptive and exteroceptive information.