Four Simple Proprioceptive Estimators for Legged Robots

Explainable & Ethical AI

Published: arXiv: 2605.23100v1

Authors

Frank Dellaert Chiyun Noh Varun Agrawal Ayoung Kim

Abstract

Legged robots carry an IMU, but the inertial solution drifts because consumer-grade IMUs are noisy. However, the feet create intermittent contacts with the environment that can be used to mitigate that drift. This report develops a sequence of increasingly expressive legged robot state estimators that leverage this. In all cases, the floating-base state comprises attitude, position, velocity, and IMU biases. To model foot contacts, we start from the contact-aided invariant EKF of Hartley et al., albeit at a reduced contact update rate. This is then augmented by replacing the measurement update by a small factor graph. Finally, we turn the same factors into a fixed-lag smoother with contact-episode footholds, with and without an evolving IMU bias. To facilitate reproducibility and further research in proprioceptive legged odometry, all four variants are available in GTSAM (Dellaert et. al), and we additionally provide a ROS2-compatible implementation.

Paper Summary

Problem

Legged robots, like robots that walk on two or four legs, have a hard time figuring out where they are and how they're moving. This is because the sensors they use to measure movement, called inertial measurement units (IMUs), are not very accurate and can drift over time. At the same time, the feet of the robot create intermittent contacts with the environment, which can be used to help estimate the robot's position and movement.

Key Innovation

This research paper presents four new and simple ways to estimate the state (position, velocity, and orientation) of a legged robot using proprioceptive sensors (IMUs) and intermittent contact information from the feet. The key innovation is the development of a sequence of increasingly expressive state estimators that leverage this information. These estimators are designed to be simpler and more efficient than previous methods, making them more suitable for real-time applications.

Practical Impact

The practical impact of this research is that it provides a more accurate and efficient way to estimate the state of a legged robot, which is essential for tasks such as navigation, manipulation, and control. This can be applied in various real-world scenarios, such as search and rescue, manufacturing, and healthcare. The proposed estimators can be used without external sensors like radar, LiDAR, or cameras, making them more robust and cost-effective.

Analogy / Intuitive Explanation

Think of a legged robot like a human trying to navigate a new city. The IMU is like a compass that gives you an idea of your direction, but it can be inaccurate and drift over time. The intermittent contact information from the feet is like getting a GPS signal from a satellite, which can help you correct your course and get a more accurate location. The proposed estimators are like a sophisticated navigation system that combines the information from the compass and the GPS signal to give you a precise location and direction.

Paper Information

Categories:

cs.RO

Published Date:

arXiv ID:

2605.23100v1

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