Deep learning based decomposition for visual navigation in industrial platforms

Djenouri, Youcef; Hatleskog, Johan; Hjelmervik, Jon M.; Bjørne, Elias; Utstumo, Trygve; Mobarhan, Milad Hobbi

doi:10.1007/s10489-021-02908-z

Cited by 10 publications

(4 citation statements)

References 38 publications

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“…There are a few similar contributions in the literature [59]- [62]. Djenouri et al [59], [60] proposed Decomposition Convolution Neural Network and vocabulary Forest (DCNN-vForest), where the first step does the extraction of regional and global CNN features. In the second step, these features are clustered using the Kmeans algorithm.…”

Section: ) Deep Learning-based Image Matching Methodsmentioning

confidence: 99%

A Cross-Platform HD Dataset and a Two-Step Framework for Robust Aerial Image Matching

2022

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Image matching has been an active research area in the computer vision community over the past decades. Significant advances in image matching algorithms have attracted attention from many emerging applications. However, aerial image matching remains demanding due to the variety of airborne platforms and onboard electro-optic sensors, long operational ranges, limited datasets and resources, and constrained operating environments. We present two contributions in this work to overcome these challenges: a) an upgraded cross-platform image dataset built over images taken from an aircraft and satellite and b) a two-step cross-platform image matching framework. Our dataset considers several practical scenarios in cross-platform matching and semantic segmentation. The first step in our two-step matching framework performs coarse-matching using a lightweight convolutional neural network (CNN) with help from aircraft instantaneous parameters. In the second step, we fine-tune standard off-the-shelf image matching algorithms by exploiting spectral, temporal and flow features followed by cluster analysis. We validate our proposed matching framework over our dataset, two publicly available aerial cross-platform datasets, and a derived dataset using various standard evaluation methodologies. Specifically, we show that both steps in our proposed two-step framework help to improve the matching performance in the crossplatform image matching scenario.

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Section: ) Deep Learning-based Image Matching Methodsmentioning

confidence: 99%

A Cross-Platform HD Dataset and a Two-Step Framework for Robust Aerial Image Matching

2022

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“…Furthermore, in the study conducted by Djenouri et al Djenouri et al (2022), the utilization of CNN-based networks for feature extraction from images in the context of a visual navigation system has been explored. This approach has proven effective in extracting discriminative visual cues, facilitating efficient and robust navigation.…”

Section: Feature Extractionmentioning

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: Feature Extractionmentioning

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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Deep learning based decomposition for visual navigation in industrial platforms

Cited by 10 publications

References 38 publications

A Cross-Platform HD Dataset and a Two-Step Framework for Robust Aerial Image Matching

A Cross-Platform HD Dataset and a Two-Step Framework for Robust Aerial Image Matching

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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