A Simple Self-Supervised IMU Denoising Method for Inertial Aided Navigation

Yuan, Kaiwen; Wang, Z. Jane

doi:10.1109/lra.2023.3234778

Cited by 8 publications

(4 citation statements)

References 22 publications

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“…URWalking: Indoor Navigation for Research and Daily Use [21] Indoor Navigation A Simple Self-Supervised IMU Denoising Method For Inertial Aided Navigation [22] Inertial Navigation Multi-Scale Fully Convolutional Network-Based Semantic Segmentation for Mobile Robot Navigation [23] Visual Navigation Drone Navigation Using Octrees and Object Recognition for Intelligent Inspections [24] Visual Navigation Deep learning-enabled fusion to bridge GPS outages for INS/GPS integrated navigation [25] Inertial Navigation Deep learning based wireless localization for indoor navigation [26] Indoor Navigation Efficient and robust LiDAR-based end-to-end navigation [27] Terrain modelling End-to-End Deep Learning Framework for Real-Time Inertial Attitude Estimation using 6DoF IMU [28] Inertial Navigation Table 2: Recent surveys on navigation or deep learning for navigation…”

Section: Title Applicationmentioning

confidence: 99%

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

Golroudbari¹,

Sabour²

2023

Preprint

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This review paper presents a comprehensive overview of end-to-end deep learning frameworks used in the context of autonomous navigation, including obstacle detection, scene perception, path planning, and control. The paper aims to bridge the gap between autonomous navigation and deep learning by analyzing recent research studies and evaluating the implementation and testing of deep learning methods. It emphasizes the importance of navigation for mobile robots, autonomous vehicles, and unmanned aerial vehicles, while also acknowledging the challenges due to environmental complexity, uncertainty, obstacles, dynamic environments, and the need to plan paths for multiple agents. The review highlights the rapid growth of deep learning in engineering data science and its development of innovative navigation methods. It discusses recent interdisciplinary work related to this field and provides a brief perspective on the limitations, challenges, and potential areas of growth for deep learning methods in autonomous navigation. Finally, the paper summarizes the findings and practices at different stages, correlating existing and future methods, their applicability, scalability, and limitations. The review provides a valuable resource for researchers and practitioners working in the field of autonomous navigation and deep learning.

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Section: Title Applicationmentioning

confidence: 99%

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

Golroudbari¹,

Sabour²

2023

Preprint

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“…The authors' primary objective was to develop algorithms with higher performance and lower complexity that would allow implementation on ultra-low power artificial intelligence microcontrollers such as the Analog Devices MAX78000. Similarly, RNNs Engelsman and Klein (2022) have been used to remove noise from accelerometer measurements, Further, in Yuan and Wang (2023), authors suggested a novel approach for IMU denoising called IMUDB using selfsupervised learning and future-aware inference techniques. The method was evaluated using end-to-end navigation on two benchmark datasets, EuRoC and TUM-VI, and demonstrated promising results.…”

Section: Signal Processingmentioning

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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“…Research has explored conventional signal-processing tools [19]. Recent deep-learning developments allowed for the achievement of promising results using supervised learning [20], and selfsupervised learning that diminishes the need for specialized datasets [21].…”

Section: Related Workmentioning

confidence: 99%

A Novel Method for Land Vehicle Positioning: Invariant Kalman Filters and Deep-Learning-Based Radar Velocity Estimation

Araujo¹,

Elhabiby²,

Givigi³

et al. 2023

Preprint

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<p>Autonomous and intelligent vehicles are multi-sensor systems operating in various environments and conditions. Due to their characteristics, inertial measurement units (IMUs) are typically the core component of such systems. However, these sensors rapidly accumulate errors due to biases and noise, degrading the positioning solution. Therefore, this paper presents a positioning solution that only uses three gyroscopes and one radar. The proposed method was tested using low-cost sensors in different scenarios, such as open-sky, urban and indoor areas. The key components of the method are the invariant Kalman filters and the use of deep neural networks to estimate the forward velocity of the car using the radar readings. The method was tested on a custom dataset, and our integrated solution accurately estimates the vehicle's 3D position, velocity, and orientation. We achieved, on average, a 1.45% translational error in the tested scenarios, making the proposed method a robust alternative to current IMU-based positioning methods.</p>

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A Simple Self-Supervised IMU Denoising Method for Inertial Aided Navigation

Cited by 8 publications

References 22 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

A Novel Method for Land Vehicle Positioning: Invariant Kalman Filters and Deep-Learning-Based Radar Velocity Estimation

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