Legged Robot State Estimation With Invariant Extended Kalman Filter Using Neural Measurement Network
This addresses the problem of accurate proprioceptive state estimation for legged robots, particularly in challenging terrains, but is incremental as it builds on existing model-based and learning-based approaches.
The paper tackles state estimation for legged robots by combining a neural measurement network with an invariant extended Kalman filter, showing that it significantly reduces position drift on various terrains like flat, debris, soft, and slippery.
This paper introduces a novel proprioceptive state estimator for legged robots that combines model-based filters and deep neural networks. Recent studies have shown that neural networks such as multi-layer perceptron or recurrent neural networks can estimate the robot states, including contact probability and linear velocity. Inspired by this, we develop a state estimation framework that integrates a neural measurement network (NMN) with an invariant extended Kalman filter. We show that our framework improves estimation performance in various terrains. Existing studies that combine model-based filters and learning-based approaches typically use real-world data. However, our approach relies solely on simulation data, as it allows us to easily obtain extensive data. This difference leads to a gap between the learning and the inference domain, commonly referred to as a sim-to-real gap. We address this challenge by adapting existing learning techniques and regularization. To validate our proposed method, we conduct experiments using a quadruped robot on four types of terrain: \textit{flat}, \textit{debris}, \textit{soft}, and \textit{slippery}. We observe that our approach significantly reduces position drift compared to the existing model-based state estimator.