Beyond Handcrafted Features: A Deep Learning Framework for Optical Flow and SLAM
This paper presents a novel approach for visual Simultaneous Localization and Mapping (SLAM) using Convolution Neural Networks (CNNs) for robust map creation. Traditional SLAM methods rely on handcrafted features, which are susceptible to viewpoint changes, occlusions, and illumination variations. T...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/11/5/155 |
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| _version_ | 1849327012454334464 |
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| author | Kamran Kazi Arbab Nighat Kalhoro Farida Memon Azam Rafique Memon Muddesar Iqbal |
| author_facet | Kamran Kazi Arbab Nighat Kalhoro Farida Memon Azam Rafique Memon Muddesar Iqbal |
| author_sort | Kamran Kazi |
| collection | DOAJ |
| description | This paper presents a novel approach for visual Simultaneous Localization and Mapping (SLAM) using Convolution Neural Networks (CNNs) for robust map creation. Traditional SLAM methods rely on handcrafted features, which are susceptible to viewpoint changes, occlusions, and illumination variations. This work proposes a method that leverages the power of CNNs by extracting features from an intermediate layer of a pre-trained model for optical flow estimation. We conduct an extensive search for optimal features by analyzing the offset error across thousands of combinations of layers and filters within the CNN. This analysis reveals a specific layer and filter combination that exhibits minimal offset error while still accounting for viewpoint changes, occlusions, and illumination variations. These features, learned by the CNN, are demonstrably robust to environmental challenges that often hinder traditional handcrafted features in SLAM tasks. The proposed method is evaluated on six publicly available datasets that are widely used for bench-marking map estimation and accuracy. Our method consistently achieved the lowest offset error compared to traditional handcrafted feature-based approaches on all six datasets. This demonstrates the effectiveness of CNN-derived features for building accurate and robust maps in diverse environments. |
| format | Article |
| id | doaj-art-9e8260787e8a4fdf9eaeeb76ca71b48a |
| institution | Kabale University |
| issn | 2313-433X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj-art-9e8260787e8a4fdf9eaeeb76ca71b48a2025-08-20T03:47:59ZengMDPI AGJournal of Imaging2313-433X2025-05-0111515510.3390/jimaging11050155Beyond Handcrafted Features: A Deep Learning Framework for Optical Flow and SLAMKamran Kazi0Arbab Nighat Kalhoro1Farida Memon2Azam Rafique Memon3Muddesar Iqbal4Institute of Information and Communication Technologies, Mehran University of Engineering and Technology, Jamshoro 76062, PakistanInstitute of Information and Communication Technologies, Mehran University of Engineering and Technology, Jamshoro 76062, PakistanDepartment of Electronic Engineering, Mehran University of Engineering and Technology, Jamshoro 76062, PakistanDepartment of Electronic Engineering, Mehran University of Engineering and Technology, Jamshoro 76062, PakistanRenewable Energy Laboratory, College of Engineering, Prince Sultan University, Riyadh 11586, Saudi ArabiaThis paper presents a novel approach for visual Simultaneous Localization and Mapping (SLAM) using Convolution Neural Networks (CNNs) for robust map creation. Traditional SLAM methods rely on handcrafted features, which are susceptible to viewpoint changes, occlusions, and illumination variations. This work proposes a method that leverages the power of CNNs by extracting features from an intermediate layer of a pre-trained model for optical flow estimation. We conduct an extensive search for optimal features by analyzing the offset error across thousands of combinations of layers and filters within the CNN. This analysis reveals a specific layer and filter combination that exhibits minimal offset error while still accounting for viewpoint changes, occlusions, and illumination variations. These features, learned by the CNN, are demonstrably robust to environmental challenges that often hinder traditional handcrafted features in SLAM tasks. The proposed method is evaluated on six publicly available datasets that are widely used for bench-marking map estimation and accuracy. Our method consistently achieved the lowest offset error compared to traditional handcrafted feature-based approaches on all six datasets. This demonstrates the effectiveness of CNN-derived features for building accurate and robust maps in diverse environments.https://www.mdpi.com/2313-433X/11/5/155CNN optical flow estimationfeature extractionmap buildingoffset errorvisual SLAM |
| spellingShingle | Kamran Kazi Arbab Nighat Kalhoro Farida Memon Azam Rafique Memon Muddesar Iqbal Beyond Handcrafted Features: A Deep Learning Framework for Optical Flow and SLAM Journal of Imaging CNN optical flow estimation feature extraction map building offset error visual SLAM |
| title | Beyond Handcrafted Features: A Deep Learning Framework for Optical Flow and SLAM |
| title_full | Beyond Handcrafted Features: A Deep Learning Framework for Optical Flow and SLAM |
| title_fullStr | Beyond Handcrafted Features: A Deep Learning Framework for Optical Flow and SLAM |
| title_full_unstemmed | Beyond Handcrafted Features: A Deep Learning Framework for Optical Flow and SLAM |
| title_short | Beyond Handcrafted Features: A Deep Learning Framework for Optical Flow and SLAM |
| title_sort | beyond handcrafted features a deep learning framework for optical flow and slam |
| topic | CNN optical flow estimation feature extraction map building offset error visual SLAM |
| url | https://www.mdpi.com/2313-433X/11/5/155 |
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