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A Hybrid Neural-Assisted Unscented Kalman Filter for Unmanned Ground Vehicle Navigation

Gal Versano, Itzik Klein

Abstract

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.

A Hybrid Neural-Assisted Unscented Kalman Filter for Unmanned Ground Vehicle Navigation

Abstract

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 compared to the adaptive model-based approach. Thus, offering a scalable and a more robust solution for unmanned ground vehicles navigation tasks.
Paper Structure (24 sections, 33 equations, 7 figures, 8 tables, 1 algorithm)

This paper contains 24 sections, 33 equations, 7 figures, 8 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Problem Formulation
  3. The UKF Algorithm
  4. Initialization
  5. Sigma Points
  6. Prediction step
  7. Update step
  8. INS/GNSS Fusion
  9. Error-State Dynamics
  10. Measurement Model
  11. Model-Based Adaptive Noise Covariance Estimation
  12. Proposed Approach
  13. Simulated Data Generation
  14. DNN Architecture
  15. Training Procedure
  16. ...and 9 more sections

Figures (7)

  • Figure 1: Our proposed ANPMN-UKF implemented on the GNSS/INS fusion scansion for unmanned ground vehicles.
  • Figure 2: Examples of different simulated trajectories in 2D-view showing rectangular, sine wave, and circle trajectories.
  • Figure 3: Our dual-usage deep neural network architecture for the estimation of the inertial sensor measurement (input/output in light blue) and position measurement (input/output in pink) standard deviations.
  • Figure 4: (a) ROOAD dataset chustz2021rooad with the Waterhod-UGV platformclearpath_warthog, (b) Our recorded dataset using the ROSbot platform husarion_rosbot_xl_manual and Arazim's IMU EX-300 arazim_ex300 , (c) Hong-Kong dataset hsu2023hongkong recorded using a car.
  • Figure 5: (a) 3D plot of Trajectory 1 showing the GT and our estimated trajectories. (b) 2D plot of Trajectory 1 showing the GT and our estimated trajectories.
  • ...and 2 more figures