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

翻译：腿式机器人的状态估计因其高度动态的运动及传感器精度的限制而颇具挑战。通过融合卡尔曼滤波、优化与基于学习的模态，我们提出了一种混合解决方案，结合本体感知与外部感知信息来估计机器人躯干的状态。利用关节编码器和惯性测量单元的测量值，我们的卡尔曼滤波器通过一个单刚体模型得到增强，该模型融入了来自凸模型预测控制优化的地面反力控制输出。通过门控循环单元，该估计进一步得到优化，其中还考虑了来自应用于深度图像的视觉Transformer自编码器的语义信息与机器人高度。这一框架不仅提供了精确的机器人状态估计（包括不确定性评估），还能通过学习最小化由传感器测量和模型简化带来的非线性误差。所提出的方法在一台四足机器人上于多种地形上进行了硬件评估，相较于我们的VIO SLAM基线，均方根误差改善了65%。代码示例：https://github.com/AlexS28/OptiState