無人地上車両ナビゲーションのためのハイブリッドニューラル支援アンセンテッドカルマンフィルタ
A Hybrid Neural-Assisted Unscented Kalman Filter for Unmanned Ground Vehicle Navigation
慣性センサとGNSSの融合に用いるUKFのノイズ共分散を、シミュレーションデータのみで訓練した深層ニューラルネットワークが実測値から予測するハイブリッド推定手法を提案し、3データセットで位置精度を向上させた。
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著者: Gal Versano, Itzik Klein
分類: cs.RO
原文アブストラクト
Modern autonomous navigation for unmanned ground vehicles relies on different estimators to fuse inertial sensors and GNSS measurements. However, the constant noise covariance matrices often struggle to account for dynamic real-world conditions. In this work we propose a hybrid estimation framework that bridges classical state estimation foundations with modern deep learning approaches. Instead of altering the fundamental unscented Kalman filter equations, a dedicated deep neural network is developed to predict the process and measurement noise uncertainty directly from raw inertial and GNSS measurements. We present a sim2real approach, with training performed only on simulative data. In this manner, we offer perfect ground truth data and relieves the burden of extensive data recordings. To evaluate our proposed approach and examine its generalization capabilities, we employed a 160-minutes test set from three datasets each with different types of vehicles (off-road vehicle, passenger car, and mobile robot), inertial sensors, road surface, and environmental conditions. We demonstrate across the three datasets a position improvement of $12.7\%$ compared to the adaptive model-based approach. Thus, offering a scalable and a more robust solution for unmanned ground vehicles navigation tasks.
