This paper reports on developing a real-time invariant proprioceptive robot state estimation framework called DRIFT. A didactic introduction to invariant Kalman filtering is provided to make this cutting-edge symmetry-preserving approach accessible to a broader range of robotics applications. Furthermore, this work dives into the development of a proprioceptive state estimation framework for dead reckoning that only consumes data from an onboard inertial measurement unit and kinematics of the robot, with two optional modules, a contact estimator and a gyro filter for low-cost robots, enabling a significant capability on a variety of robotics platforms to track the robot's state over long trajectories in the absence of perceptual data. Extensive real-world experiments using a legged robot, an indoor wheeled robot, a field robot, and a full-size vehicle, as well as simulation results with a marine robot, are provided to understand the limits of DRIFT.

翻译：本文报告了一种名为DRIFT的实时不变性本体感知机器人状态估计框架的开发。文中提供了不变性卡尔曼滤波的教学式介绍，旨在使这种前沿的对称性保持方法更易于广泛机器人应用领域所接受。此外，本研究深入探讨了一种用于航位推算的本体感知状态估计框架，该框架仅依赖板载惯性测量单元和机器人运动学数据，并配备两个可选模块：接触估计器和适用于低成本机器人的陀螺滤波器，从而使得各种机器人平台在缺乏感知数据的情况下，能够实现沿长轨迹跟踪机器人状态的显著能力。通过使用腿式机器人、室内轮式机器人、野外机器人和全尺寸车辆进行的广泛真实世界实验，以及使用海洋机器人的仿真结果，揭示了DRIFT的极限能力。