State estimation for legged robots is challenging due to their highly dynamic motion and limitations imposed by sensor accuracy. By integrating Kalman filtering, optimization, and learning-based modalities, we propose a hybrid solution that combines proprioception and exteroceptive information for estimating the state of the robot's trunk. Leveraging joint encoder and IMU measurements, our Kalman filter is enhanced through a single-rigid body model that incorporates ground reaction force control outputs from convex Model Predictive Control optimization. The estimation is further refined through Gated Recurrent Units, which also considers semantic insights and robot height from a Vision Transformer autoencoder applied on depth images. This framework not only furnishes accurate robot state estimates, including uncertainty evaluations, but can minimize the nonlinear errors that arise from sensor measurements and model simplifications through learning. The proposed methodology is evaluated in hardware using a quadruped robot on various terrains, yielding a 65% improvement on the Root Mean Squared Error compared to our VIO SLAM baseline. Code example: https://github.com/AlexS28/OptiState

通过在四足机器人上评估，我们提出了一种混合解决方案，通过结合本体感知和外部感知信息，利用Kalman滤波、优化和基于学习的模态，对机器人躯干状态进行估计，从而实现准确的机器人状态估计，并减少传感器测量和模型简化带来的非线性误差。

OptiState: 采用具有基于 Transformer 的视觉和卡尔曼滤波的门控网络对四足机器人进行状态估计