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arXiv · 2311.04320

Proprioceptive Invariant Robot State Estimation

Tzu-Yuan Lin·Tingjun Li·Wenzhe Tong·Maani Ghaffari
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Abstract

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.

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Tzu-Yuan Lin, Tingjun Li, Wenzhe Tong, Maani Ghaffari. 2023-11-07. Proprioceptive Invariant Robot State Estimation. https://arxiv.org/abs/2311.04320

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