Reinforcement Learning for Orientation Estimation Using Inertial Sensors with Performance Guarantee

Hu, Liang; Tang, Yujie; Zhou, Zhipeng; Pan, Wei

doi:10.1109/icra48506.2021.9561440

Cited by 9 publications

(3 citation statements)

References 18 publications

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“…In the domain of orientation estimation, Hu et al Hu et al (2021a) propose a deep reinforcement learning (DRL) algorithm that combines inertial sensors with a magnetometer. The algorithm utilizes Lyapunov's method from control theory to ensure convergence of orientation estimation errors.…”

Section: Ta B L E 1 3 Imu Datasetsmentioning

confidence: 99%

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review

Asgharpoor

Sabour

2023

Preprint

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This review article presents recent advancements in deep learning method-ologies and applications for autonomous navigation. It analyzes state-of-the-art deep learning frameworks used in tasks like signal processing, attitude estimation, obstacle detection, scene perception, and path planning. The implementation and testing methodologies of these approaches are critically evaluated, highlighting their strengths, limitations, and areas for further development. The review emphasizes the interdisciplinary nature of autonomous navigation and addresses challenges posed by dynamic and complex environments, uncertainty, and obstacles. With a particular focus on mobile robots, self-driving cars, unmanned aerial vehicles, and space vehicles to underscore the importance of navigation in these domains. By synthesizing findings from multiple studies, the review aims to be a valuable resource for researchers and practitioners, contributing to the advancement of novel approaches. Key aspects covered include the classification of deep learning applications, recent advancements in methods, general applications in the field, innovations, challenges, and limitations associated with learning-based navigation systems. This review also explores current research trends and future directions in the field. This extensive overview, initiated in 2020, provides a valuable resource for researchers of all levels, from seasoned experts to newcomers. Its main purpose is to streamline the process of identifying , evaluating, and interpreting relevant research, ultimately contributing to the progress and development of autonomous navigation technologies.

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Section: Ta B L E 1 3 Imu Datasetsmentioning

confidence: 99%

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review

Asgharpoor

Sabour

2023

Preprint

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Section: Ta B L E 1 3 Imu Datasetsmentioning

confidence: 99%

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review

Asgharpoor

Sabour

2023

Preprint

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This review article presents recent advancements in deep learning methodologies and applications for autonomous nav- igation. It analyzes state-of-the-art deep learning frameworks used in tasks like signal processing, attitude estimation, ob- stacle detection, scene perception, and path planning. The implementation and testing methodologies of these approaches are critically evaluated, highlighting their strengths, limita- tions, and areas for further development. The review em- phasizes the interdisciplinary nature of autonomous naviga- tion and addresses challenges posed by dynamic and com- plex environments, uncertainty, and obstacles. With a par- ticular focus on mobile robots, self-driving cars, unmanned aerial vehicles, and space vehicles to underscore the impor- tance of navigation in these domains. By synthesizing find- ings from multiple studies, the review aims to be a valuable resource for researchers and practitioners, contributing to the advancement of novel approaches. Key aspects covered include the classification of deep learning applications, re- cent advancements in methods, general applications in the field, innovations, challenges, and limitations associated with learning-based navigation systems. This review also explores current research trends and future directions in the field. This extensive overview, initiated in 2020, provides a valuable re- source for researchers of all levels, from seasoned experts to newcomers. Its main purpose is to streamline the process of identifying, evaluating, and interpreting relevant research, ultimately contributing to the progress and development of autonomous navigation technologies.

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“…Inspired by the strong representation capability of neural networks [ 38 , 39 ], several filters based on reinforcement learning have been studied. Hu et al [ 40 ] designed an attitude estimator by combining Lyapunov’s method and the deep reinforcement learning algorithm. Tang et al [ 41 ] combined the classic EKF with the deep reinforcement learning algorithm to realize the attitude estimation of the navigation system, which introduced a gain matrix of the residual and took it as the action to learn.…”

Section: Introductionmentioning

confidence: 99%

Noise-Adaption Extended Kalman Filter Based on Deep Deterministic Policy Gradient for Maneuvering Targets

Tang

Guo

2022

Sensors

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Although there have been numerous studies on maneuvering target tracking, few studies have focused on the distinction between unknown maneuvers and inaccurate measurements, leading to low accuracy, poor robustness, or even divergence. To this end, a noise-adaption extended Kalman filter is proposed to track maneuvering targets with multiple synchronous sensors. This filter avoids the simultaneous adjustment of the process model and measurement model without distinction. Instead, the maneuver detection based on the Dempster-Shafer evidence theory is constructed to achieve the reliable distinction between unknown maneuvers and inaccurate measurements by fusing multi-sensor information, which effectively improves the robustness of the filter. Moreover, the adaptive estimation of the process noise covariance is modeled by a Markovian decision process with a proper reward function. Deep deterministic policy gradient is designed to obtain the optimal process noise covariance by taking the innovation as the state and the compensation factor as the action. Furthermore, the recursive estimation of the measurement noise covariance is applied to modify a priori measurement noise covariance of the corresponding sensor. Finally, the fusion algorithm is developed for the global estimation. Simulation experiments are carried out in two scenarios, and simulation results illustrate the feasibility and superiority of the proposed algorithm.

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Reinforcement Learning for Orientation Estimation Using Inertial Sensors with Performance Guarantee

Cited by 9 publications

References 18 publications

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review

Noise-Adaption Extended Kalman Filter Based on Deep Deterministic Policy Gradient for Maneuvering Targets

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