A 3D Reconstruction Method Using Multisensor Fusion in Large-Scale Indoor Scenes
Autor: | Fang Wan, Gu Panlong, Dianguo Yu, Wei Wang, Yu Bangguo, Fengyu Zhou |
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Jazyk: | angličtina |
Rok vydání: | 2020 |
Předmět: |
0209 industrial biotechnology
Multidisciplinary General Computer Science Article Subject Computer science business.industry 3D reconstruction Point cloud ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION QA75.5-76.95 02 engineering and technology Simultaneous localization and mapping 020901 industrial engineering & automation Lidar Robustness (computer science) Electronic computers. Computer science 0202 electrical engineering electronic engineering information engineering Robot 020201 artificial intelligence & image processing Computer vision Artificial intelligence business |
Zdroj: | Complexity, Vol 2020 (2020) |
ISSN: | 1076-2787 |
DOI: | 10.1155/2020/6973790 |
Popis: | RGBD camera-based VSLAM (Visual Simultaneous Localization and Mapping) algorithm is usually applied to assist robots with real-time mapping. However, due to the limited measuring principle, accuracy, and distance of the equipped camera, this algorithm has typical disadvantages in the large and dynamic scenes with complex lightings, such as poor mapping accuracy, easy loss of robot position, and much cost on computing resources. Regarding these issues, this paper proposes a new method of 3D interior construction, which combines laser radar and an RGBD camera. Meanwhile, it is developed based on the Cartographer laser SLAM algorithm. The proposed method mainly takes two steps. The first step is to do the 3D reconstruction using the Cartographer algorithm and RGBD camera. It firstly applies the Cartographer algorithm to calculate the pose of the RGBD camera and to generate a submap. Then, a real-time 3D point cloud generated by using the RGBD camera is inserted into the submap, and the real-time interior construction is finished. The second step is to improve Cartographer loop-closure quality by the visual loop-closure for the sake of correcting the generated map. Compared with traditional methods in large-scale indoor scenes, the proposed algorithm in this paper shows higher precision, faster speed, and stronger robustness in such contexts, especially with complex light and dynamic objects, respectively. |
Databáze: | OpenAIRE |
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