Legged Robot State Estimation With Invariant Extended Kalman Filter Using Neural Measurement Network

• URL: http://arxiv.org/abs/2402.00366v1
• Date: Thu, 1 Feb 2024 06:06:59 GMT
• Title: Legged Robot State Estimation With Invariant Extended Kalman Filter Using Neural Measurement Network
• Authors: Donghoon Youm, Hyunsik Oh, Suyoung Choi, Hyeongjun Kim, Jemin Hwangbo
• Abstract summary: We develop a state estimation framework that integrates a neural measurement network (NMN) with an invariant extended Kalman filter. Our approach significantly reduces position drift compared to the existing model-based state estimator.
• Score: 2.0405494347486197
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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