Legged Robot State Estimation Using Invariant Neural-Augmented Kalman Filter with a Neural Compensator

Abstract

This paper presents an algorithm to improve state estimation for legged robots. Among existing model-based state estimation methods for legged robots, the contact-aided invariant extended Kalman filter defines the state on a Lie group to preserve invariance, thereby significantly accelerating convergence. It achieves more accurate state estimation by leveraging contact information as measurements for the update step. However, when the model exhibits strong nonlinearity, the estimation accuracy decreases. Such nonlinearities can cause initial errors to accumulate and lead to large drifts over time. To address this issue, we propose compensating for errors by augmenting the Kalman filter with an artificial neural network serving as a nonlinear function approximator. Furthermore, we design this neural network to respect the Lie group structure to ensure invariance, resulting in our proposed Invariant Neural-Augmented Kalman Filter (InNKF). The proposed algorithm offers improved state estimation performance by combining the strengths of model-based and learning-based approaches. Project webpage: https://seokju-lee.github.io/innkf_webpage

arXiv comment8 pages, 10 figures, Accepted to IROS 2025

References in corpus (5)

• Learning Quadrupedal Locomotion over Challenging Terrain
• Learning robust perceptive locomotion for quadrupedal robots in the wild
• KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics
• Concurrent Training of a Control Policy and a State Estimator for Dynamic and Robust Legged Locomotion
• Robust Legged Robot State Estimation Using Factor Graph Optimization