Proprioceptive Invariant Robot State Estimation

TL;DR

This paper introduces DRIFT, a real-time invariant proprioceptive state estimation framework using invariant Kalman filtering, enabling long-term robot state tracking solely from onboard inertial and kinematic data across diverse platforms.

Contribution

The work develops a novel proprioceptive state estimation method based on invariant Kalman filtering, accessible to various robotics applications, with optional modules for low-cost robots and extensive real-world validation.

Findings

Effective long-term state tracking demonstrated on multiple robot types.

Invariant Kalman filtering improves robustness and accuracy.

Framework operates with minimal sensor data, suitable for low-cost robots.

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.