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: this https URL

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。