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Proprioceptive Robot State Estimation

Published on:

7 November 2023

Primary Category:


Paper Authors:

Tzu-Yuan Lin,

Tingjun Li,

Wenzhe Tong,

Maani Ghaffari


Key Details

Presents DRIFT, a real-time proprioceptive state estimation framework

Uses invariant Kalman filtering for improved consistency

Includes tutorial introduction to invariant filtering

Develops dead reckoning modules for diverse robot types

Adds contact estimation and gyro filtering for low-cost robots

Shows accurate long-term tracking without perception

AI generated summary

Proprioceptive Robot State Estimation

This paper presents DRIFT, a real-time proprioceptive robot state estimation framework. DRIFT uses only data from onboard sensors like IMUs and joint encoders to track a robot's orientation, velocity, and position. The method is based on invariant Kalman filtering, which exploits symmetries in the state space for improved consistency. The authors provide a tutorial introduction to invariant filtering, making the approach more accessible. They develop DRIFT modules for dead reckoning on various robots, including optional contact estimation and gyro filtering to enable low-cost platforms. Extensive experiments on legged, wheeled, and marine robots demonstrate DRIFT's capabilities for long-horizon, perception-free state tracking.

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