Building a Real-Time 2D Lidar Using Deep Learning
Applying deep learning methods, this paper addresses depth prediction problem resulting from single monocular images. A vector of distances is predicted instead of a whole image matrix. A vector-only prediction decreases training overhead and prediction periods and requires less resources (memory, C...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Robotics |
| Online Access: | http://dx.doi.org/10.1155/2021/6652828 |
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| _version_ | 1849696306976522240 |
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| author | Nadim Arubai Omar Hamdoun Assef Jafar |
| author_facet | Nadim Arubai Omar Hamdoun Assef Jafar |
| author_sort | Nadim Arubai |
| collection | DOAJ |
| description | Applying deep learning methods, this paper addresses depth prediction problem resulting from single monocular images. A vector of distances is predicted instead of a whole image matrix. A vector-only prediction decreases training overhead and prediction periods and requires less resources (memory, CPU). We propose a module which is more time efficient than the state-of-the-art modules ResNet, VGG, FCRN, and DORN. We enhanced the network results by training it on depth vectors from other levels (we get a new level by changing the Lidar tilt angle). The predicted results give a vector of distances around the robot, which is sufficient for the obstacle avoidance problem and many other applications. |
| format | Article |
| id | doaj-art-c81fc56183524c0a9f016f48a52dbd24 |
| institution | DOAJ |
| issn | 1687-9600 1687-9619 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Robotics |
| spelling | doaj-art-c81fc56183524c0a9f016f48a52dbd242025-08-20T03:19:29ZengWileyJournal of Robotics1687-96001687-96192021-01-01202110.1155/2021/66528286652828Building a Real-Time 2D Lidar Using Deep LearningNadim Arubai0Omar Hamdoun1Assef Jafar2Higher Institute for Applied Sciences and Technology, Damascus, SyriaHigher Institute for Applied Sciences and Technology, Damascus, SyriaHigher Institute for Applied Sciences and Technology, Damascus, SyriaApplying deep learning methods, this paper addresses depth prediction problem resulting from single monocular images. A vector of distances is predicted instead of a whole image matrix. A vector-only prediction decreases training overhead and prediction periods and requires less resources (memory, CPU). We propose a module which is more time efficient than the state-of-the-art modules ResNet, VGG, FCRN, and DORN. We enhanced the network results by training it on depth vectors from other levels (we get a new level by changing the Lidar tilt angle). The predicted results give a vector of distances around the robot, which is sufficient for the obstacle avoidance problem and many other applications.http://dx.doi.org/10.1155/2021/6652828 |
| spellingShingle | Nadim Arubai Omar Hamdoun Assef Jafar Building a Real-Time 2D Lidar Using Deep Learning Journal of Robotics |
| title | Building a Real-Time 2D Lidar Using Deep Learning |
| title_full | Building a Real-Time 2D Lidar Using Deep Learning |
| title_fullStr | Building a Real-Time 2D Lidar Using Deep Learning |
| title_full_unstemmed | Building a Real-Time 2D Lidar Using Deep Learning |
| title_short | Building a Real-Time 2D Lidar Using Deep Learning |
| title_sort | building a real time 2d lidar using deep learning |
| url | http://dx.doi.org/10.1155/2021/6652828 |
| work_keys_str_mv | AT nadimarubai buildingarealtime2dlidarusingdeeplearning AT omarhamdoun buildingarealtime2dlidarusingdeeplearning AT assefjafar buildingarealtime2dlidarusingdeeplearning |