DogLegs: Robust Proprioceptive State Estimation for Legged Robots Using Multiple Leg-Mounted IMUs

2025-03-06

AI Summary

To address the insufficient robustness of legged robot body-state estimation under extreme conditions where external sensors (e.g., LiDAR, cameras) fail, this paper proposes a tightly coupled self-perception method integrating torso-mounted IMU, multiple foot-mounted IMUs, and joint encoders. Within an extended Kalman filter framework, we innovatively formulate a collaborative error model across foot-mounted IMUs and incorporate inter-frame relative pose constraints. Leveraging foot contact detection and leg kinematic geometric constraints, the method jointly enforces zero-velocity updates and drift suppression. Experimental results demonstrate that our approach significantly outperforms conventional legged odometry relying solely on torso IMU and joint encoders across diverse unstructured terrains, achieving substantially improved body pose estimation accuracy. All experimental datasets are publicly released.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Low Level & Physics-based Vision
• Natural Language Processing: Safety and Robustness

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Robust and accurate proprioceptive state estimation of the main body is crucial for legged robots to execute tasks in extreme environments where exteroceptive sensors, such as LiDARs and cameras may become unreliable. In this paper, we propose DogLegs, a state estimation system for legged robots that fuses the measurements from a body-mounted inertial measurement unit (Body-IMU), joint encoders, and multiple leg-mounted IMUs (Leg-IMU) using an extended Kalman filter (EKF). The filter system contains the error states of all IMU frames. The Leg-IMUs are used to detect foot contact, thereby providing zero velocity measurements to update the state of the Leg-IMU frames. Additionally, we compute the relative position constraints between the Body-IMU and Leg-IMUs by the leg kinematics and use them to update the main body state and reduce the error drift of the individual IMU frames. Field experimental results have shown that our proposed system can achieve better state estimation accuracy compared to the traditional leg odometry method (using only Body-IMU and joint encoders) across different terrains. We make our datasets publicly available to benefit the research community.

Problem

• Improves legged robot state estimation in extreme environments
• Fuses Body-IMU, Leg-IMUs, and joint encoders using EKF
• Reduces error drift and enhances accuracy across terrains

Innovation

• Fuses Body-IMU, Leg-IMUs, and joint encoders
• Uses extended Kalman filter for error correction
• Detects foot contact via Leg-IMUs for zero velocity