REINFORCEMENT LEARNING FOR AUTONOMOUS 3D DATA RETRIEVAL USING A MOBILE ROBOT
3D data retrieval is required in various fields such as an industrial monitoring, agriculture, and robotics. Recent advances in photogrammetry and computer vision allowed to perform 3D reconstruction using a set of images captured with uncalibrated camera. Such technique is commonly known as Structu...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2022-05-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B2-2022/553/2022/isprs-archives-XLIII-B2-2022-553-2022.pdf |
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| author | V. V. Kniaz V. V. Kniaz V. Mizginov A. Bordodymov P. Moshkantsev D. Novikov |
| author_facet | V. V. Kniaz V. V. Kniaz V. Mizginov A. Bordodymov P. Moshkantsev D. Novikov |
| author_sort | V. V. Kniaz |
| collection | DOAJ |
| description | 3D data retrieval is required in various fields such as an industrial monitoring, agriculture, and robotics. Recent advances in photogrammetry and computer vision allowed to perform 3D reconstruction using a set of images captured with uncalibrated camera. Such technique is commonly known as Structure-from-Motion. In this paper, we propose a reinforcement learning framework RL3D for online strong camera configuration planning onboard of a mobile robot. The mobile robot consists of a skid-steered wheeled platform, a single-board computer and an industrial camera. Our aim is developing a model that plans a set of robot location that provide a strong camera configuration. We developed an environment simulator to train our RL3D framework. The simulator was implemented using a 3D model of the indoor scene and includes a model of robot’s dynamics. We trained our framework using the simulator and evaluated it using a virtual and real environments. The results of the evaluation are encouraging and demonstrate that the controller model successfully learns simple camera configurations such as a circle around an object. |
| first_indexed | 2024-04-14T00:16:53Z |
| format | Article |
| id | doaj.art-47efdd411c8541ab8c6a48595bdb5bcb |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-04-14T00:16:53Z |
| publishDate | 2022-05-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-47efdd411c8541ab8c6a48595bdb5bcb2022-12-22T02:23:06ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342022-05-01XLIII-B2-202255355810.5194/isprs-archives-XLIII-B2-2022-553-2022REINFORCEMENT LEARNING FOR AUTONOMOUS 3D DATA RETRIEVAL USING A MOBILE ROBOTV. V. Kniaz0V. V. Kniaz1V. Mizginov2A. Bordodymov3P. Moshkantsev4D. Novikov5State Res. Institute of Aviation Systems (GosNIIAS), 125319, 7, Victorenko str., Moscow, RussiaMoscow Institute of Physics and Technology (MIPT), RussiaState Res. Institute of Aviation Systems (GosNIIAS), 125319, 7, Victorenko str., Moscow, RussiaState Res. Institute of Aviation Systems (GosNIIAS), 125319, 7, Victorenko str., Moscow, RussiaState Res. Institute of Aviation Systems (GosNIIAS), 125319, 7, Victorenko str., Moscow, RussiaState Res. Institute of Aviation Systems (GosNIIAS), 125319, 7, Victorenko str., Moscow, Russia3D data retrieval is required in various fields such as an industrial monitoring, agriculture, and robotics. Recent advances in photogrammetry and computer vision allowed to perform 3D reconstruction using a set of images captured with uncalibrated camera. Such technique is commonly known as Structure-from-Motion. In this paper, we propose a reinforcement learning framework RL3D for online strong camera configuration planning onboard of a mobile robot. The mobile robot consists of a skid-steered wheeled platform, a single-board computer and an industrial camera. Our aim is developing a model that plans a set of robot location that provide a strong camera configuration. We developed an environment simulator to train our RL3D framework. The simulator was implemented using a 3D model of the indoor scene and includes a model of robot’s dynamics. We trained our framework using the simulator and evaluated it using a virtual and real environments. The results of the evaluation are encouraging and demonstrate that the controller model successfully learns simple camera configurations such as a circle around an object.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B2-2022/553/2022/isprs-archives-XLIII-B2-2022-553-2022.pdf |
| spellingShingle | V. V. Kniaz V. V. Kniaz V. Mizginov A. Bordodymov P. Moshkantsev D. Novikov REINFORCEMENT LEARNING FOR AUTONOMOUS 3D DATA RETRIEVAL USING A MOBILE ROBOT The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | REINFORCEMENT LEARNING FOR AUTONOMOUS 3D DATA RETRIEVAL USING A MOBILE ROBOT |
| title_full | REINFORCEMENT LEARNING FOR AUTONOMOUS 3D DATA RETRIEVAL USING A MOBILE ROBOT |
| title_fullStr | REINFORCEMENT LEARNING FOR AUTONOMOUS 3D DATA RETRIEVAL USING A MOBILE ROBOT |
| title_full_unstemmed | REINFORCEMENT LEARNING FOR AUTONOMOUS 3D DATA RETRIEVAL USING A MOBILE ROBOT |
| title_short | REINFORCEMENT LEARNING FOR AUTONOMOUS 3D DATA RETRIEVAL USING A MOBILE ROBOT |
| title_sort | reinforcement learning for autonomous 3d data retrieval using a mobile robot |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B2-2022/553/2022/isprs-archives-XLIII-B2-2022-553-2022.pdf |
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