A Critical Review of Deep Learning-Based Multi-Sensor Fusion Techniques
In this review, we provide a detailed coverage of multi-sensor fusion techniques that use RGB stereo images and a sparse LiDAR-projected depth map as input data to output a dense depth map prediction. We cover state-of-the-art fusion techniques which, in recent years, have been deep learning-based m...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-12-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/23/9364 |
| _version_ | 1797462126939340800 |
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| author | Benedict Marsh Abdul Hamid Sadka Hamid Bahai |
| author_facet | Benedict Marsh Abdul Hamid Sadka Hamid Bahai |
| author_sort | Benedict Marsh |
| collection | DOAJ |
| description | In this review, we provide a detailed coverage of multi-sensor fusion techniques that use RGB stereo images and a sparse LiDAR-projected depth map as input data to output a dense depth map prediction. We cover state-of-the-art fusion techniques which, in recent years, have been deep learning-based methods that are end-to-end trainable. We then conduct a comparative evaluation of the state-of-the-art techniques and provide a detailed analysis of their strengths and limitations as well as the applications they are best suited for. |
| first_indexed | 2024-03-09T17:32:04Z |
| format | Article |
| id | doaj.art-99be72c0acfa447081a2fc1ed3bb5db4 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T17:32:04Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-99be72c0acfa447081a2fc1ed3bb5db42023-11-24T12:12:59ZengMDPI AGSensors1424-82202022-12-012223936410.3390/s22239364A Critical Review of Deep Learning-Based Multi-Sensor Fusion TechniquesBenedict Marsh0Abdul Hamid Sadka1Hamid Bahai2Institute of Digital Futures, Brunel University London, Kingston Ln, Uxbridge UB8 3PH, UKInstitute of Digital Futures, Brunel University London, Kingston Ln, Uxbridge UB8 3PH, UKInstitute of Materials and Manufacturing, Brunel University London, Kingston Ln, Uxbridge UB8 3PH, UKIn this review, we provide a detailed coverage of multi-sensor fusion techniques that use RGB stereo images and a sparse LiDAR-projected depth map as input data to output a dense depth map prediction. We cover state-of-the-art fusion techniques which, in recent years, have been deep learning-based methods that are end-to-end trainable. We then conduct a comparative evaluation of the state-of-the-art techniques and provide a detailed analysis of their strengths and limitations as well as the applications they are best suited for.https://www.mdpi.com/1424-8220/22/23/9364sensor fusionstereoLiDARdeep learning |
| spellingShingle | Benedict Marsh Abdul Hamid Sadka Hamid Bahai A Critical Review of Deep Learning-Based Multi-Sensor Fusion Techniques Sensors sensor fusion stereo LiDAR deep learning |
| title | A Critical Review of Deep Learning-Based Multi-Sensor Fusion Techniques |
| title_full | A Critical Review of Deep Learning-Based Multi-Sensor Fusion Techniques |
| title_fullStr | A Critical Review of Deep Learning-Based Multi-Sensor Fusion Techniques |
| title_full_unstemmed | A Critical Review of Deep Learning-Based Multi-Sensor Fusion Techniques |
| title_short | A Critical Review of Deep Learning-Based Multi-Sensor Fusion Techniques |
| title_sort | critical review of deep learning based multi sensor fusion techniques |
| topic | sensor fusion stereo LiDAR deep learning |
| url | https://www.mdpi.com/1424-8220/22/23/9364 |
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