Reinforcement Learning Compensated Extended Kalman Filter for Attitude Estimation

2021/07/25 by Yujie Tang, Tang, Yujie, Liang Hu +5 · 2 citations
Computer Science · Engineering · #Advanced Multi-Objective Optimization Algorithms #FOS: Computer and information sciences #Inertial Sensor and Navigation #Robotics (cs.RO) #Target Tracking and Data Fusion in Sensor Networks #cs.RO

paper · pdf · doi:10.48550/arxiv.2107.11777

This paper has been accepted by IROS 2021

openalex publication_date 2021/07/25 · arxiv created 2021/07/27 · arxiv updated 2021/07/28 · openalex created_date 2022/10/06 · openalex updated_date 2026/07/28

Abstract

Inertial measurement units are widely used in different fields to estimate the attitude. Many algorithms have been proposed to improve estimation performance. However, most of them still suffer from 1) inaccurate initial estimation, 2) inaccurate initial filter gain, and 3) non-Gaussian process and/or measurement noise. In this paper, we leverage reinforcement learning to compensate for the classical extended Kalman filter estimation, i.e., to learn the filter gain from the sensor measurements. We also analyse the convergence of the estimate error. The effectiveness of the proposed algorithm is validated on both simulated data and real data.

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