Intelligent Extraction Method of Inertial Navigation Trajectory Behavior Features Considering Road Environment

Li, Xiang; Liu, Wenbing; Chen, Qun

doi:10.1007/978-3-030-85462-1_4

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…A notable contribution in this domain is the introduction of a CNN-GRU model that focuses on extracting behavioral features from inertial navigation trajectories. This model showcases the successful integration of RNNs with CNNs, enabling the capture of temporal dependencies and enhancing the accuracy of feature extraction Li et al (2021a).…”

Section: Feature Extractionmentioning

confidence: 93%

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: Feature Extractionmentioning

confidence: 93%

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

Asgharpoor

Sabour

2023

Preprint

View full text Add to dashboard Cite

show abstract

Section: Feature Extractionmentioning

confidence: 93%

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

Asgharpoor

Sabour

2023

Preprint

View full text Add to dashboard Cite

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.

show abstract

Intelligent Extraction Method of Inertial Navigation Trajectory Behavior Features Considering Road Environment

Cited by 2 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

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