Dense RGB-D SLAM with Multiple Cameras
A multi-camera dense RGB-D SLAM (simultaneous localization and mapping) system has the potential both to speed up scene reconstruction and to improve localization accuracy, thanks to multiple mounted sensors and an enlarged effective field of view. To effectively tap the potential of the system, two...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-07-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/18/7/2118 |
| _version_ | 1798041096829272064 |
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| author | Xinrui Meng Wei Gao Zhanyi Hu |
| author_facet | Xinrui Meng Wei Gao Zhanyi Hu |
| author_sort | Xinrui Meng |
| collection | DOAJ |
| description | A multi-camera dense RGB-D SLAM (simultaneous localization and mapping) system has the potential both to speed up scene reconstruction and to improve localization accuracy, thanks to multiple mounted sensors and an enlarged effective field of view. To effectively tap the potential of the system, two issues must be understood: first, how to calibrate the system where sensors usually shares small or no common field of view to maximally increase the effective field of view; second, how to fuse the location information from different sensors. In this work, a three-Kinect system is reported. For system calibration, two kinds of calibration methods are proposed, one is suitable for system with inertial measurement unit (IMU) using an improved hand–eye calibration method, the other for pure visual SLAM without any other auxiliary sensors. In the RGB-D SLAM stage, we extend and improve a state-of-art single RGB-D SLAM method to multi-camera system. We track the multiple cameras’ poses independently and select the one with the pose minimal-error as the reference pose at each moment to correct other cameras’ poses. To optimize the initial estimated pose, we improve the deformation graph by adding an attribute of device number to distinguish surfels built by different cameras and do deformations according to the device number. We verify the accuracy of our extrinsic calibration methods in the experiment section and show the satisfactory reconstructed models by our multi-camera dense RGB-D SLAM. The RMSE (root-mean-square error) of the lengths measured in our reconstructed mode is 1.55 cm (similar to the state-of-art single camera RGB-D SLAM systems). |
| first_indexed | 2024-04-11T22:16:44Z |
| format | Article |
| id | doaj.art-d13cdde1daae4f51a9d98d7e92b28c4f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:16:44Z |
| publishDate | 2018-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d13cdde1daae4f51a9d98d7e92b28c4f2022-12-22T04:00:21ZengMDPI AGSensors1424-82202018-07-01187211810.3390/s18072118s18072118Dense RGB-D SLAM with Multiple CamerasXinrui Meng0Wei Gao1Zhanyi Hu2National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences, Beijing 100190, ChinaNational Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences, Beijing 100190, ChinaNational Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences, Beijing 100190, ChinaA multi-camera dense RGB-D SLAM (simultaneous localization and mapping) system has the potential both to speed up scene reconstruction and to improve localization accuracy, thanks to multiple mounted sensors and an enlarged effective field of view. To effectively tap the potential of the system, two issues must be understood: first, how to calibrate the system where sensors usually shares small or no common field of view to maximally increase the effective field of view; second, how to fuse the location information from different sensors. In this work, a three-Kinect system is reported. For system calibration, two kinds of calibration methods are proposed, one is suitable for system with inertial measurement unit (IMU) using an improved hand–eye calibration method, the other for pure visual SLAM without any other auxiliary sensors. In the RGB-D SLAM stage, we extend and improve a state-of-art single RGB-D SLAM method to multi-camera system. We track the multiple cameras’ poses independently and select the one with the pose minimal-error as the reference pose at each moment to correct other cameras’ poses. To optimize the initial estimated pose, we improve the deformation graph by adding an attribute of device number to distinguish surfels built by different cameras and do deformations according to the device number. We verify the accuracy of our extrinsic calibration methods in the experiment section and show the satisfactory reconstructed models by our multi-camera dense RGB-D SLAM. The RMSE (root-mean-square error) of the lengths measured in our reconstructed mode is 1.55 cm (similar to the state-of-art single camera RGB-D SLAM systems).http://www.mdpi.com/1424-8220/18/7/2118multi-cameraSLAMcalibrationRGB-D |
| spellingShingle | Xinrui Meng Wei Gao Zhanyi Hu Dense RGB-D SLAM with Multiple Cameras Sensors multi-camera SLAM calibration RGB-D |
| title | Dense RGB-D SLAM with Multiple Cameras |
| title_full | Dense RGB-D SLAM with Multiple Cameras |
| title_fullStr | Dense RGB-D SLAM with Multiple Cameras |
| title_full_unstemmed | Dense RGB-D SLAM with Multiple Cameras |
| title_short | Dense RGB-D SLAM with Multiple Cameras |
| title_sort | dense rgb d slam with multiple cameras |
| topic | multi-camera SLAM calibration RGB-D |
| url | http://www.mdpi.com/1424-8220/18/7/2118 |
| work_keys_str_mv | AT xinruimeng densergbdslamwithmultiplecameras AT weigao densergbdslamwithmultiplecameras AT zhanyihu densergbdslamwithmultiplecameras |