3D Semantic Map Construction Using Improved ORB-SLAM2 for Mobile Robot in Edge Computing Environment
Although the existing localization and mapping (SLAM) technology of indoor mobile robot has made great development, its intelligence and environmental perception ability still cannot meet the needs of service and inspection. Therefore, based on edge computing environment, a 3D semantic map construct...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9047931/ |
| _version_ | 1818853397158166528 |
|---|---|
| author | Xu Cui Chenggang Lu Jinxiang Wang |
| author_facet | Xu Cui Chenggang Lu Jinxiang Wang |
| author_sort | Xu Cui |
| collection | DOAJ |
| description | Although the existing localization and mapping (SLAM) technology of indoor mobile robot has made great development, its intelligence and environmental perception ability still cannot meet the needs of service and inspection. Therefore, based on edge computing environment, a 3D semantic map construction of mobile robot based on improved ORB-SALM2 is proposed. Firstly, the improved yolov3 algorithm is used to detect indoor objects, and then the real-time semantic segmentation network model based on deep learning is used to segment indoor objects to achieve the classification of pixel points of objects on two-dimensional images, and BAFF feature fusion algorithm is introduced to improve the accuracy of semantic segmentation model. Then, through the SLAM system, we estimate the pose of the image in the result of semantic segmentation, and use the depth information to project it into the three-dimensional environment to build the three-dimensional semantic map. Finally, the experiment platform of mobile robot is built to verify the stability of ORB-D and thermal imaging sensor registration technology, the accuracy and real-time of building three-dimensional environment thermal field map, and the accuracy of robot positioning using thermal infrared and depth image. |
| first_indexed | 2024-12-19T07:36:10Z |
| format | Article |
| id | doaj.art-2b811c6e9c5d42e692744bcef122015f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T07:36:10Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-2b811c6e9c5d42e692744bcef122015f2022-12-21T20:30:35ZengIEEEIEEE Access2169-35362020-01-018671796719110.1109/ACCESS.2020.298348890479313D Semantic Map Construction Using Improved ORB-SLAM2 for Mobile Robot in Edge Computing EnvironmentXu Cui0https://orcid.org/0000-0002-3134-4995Chenggang Lu1https://orcid.org/0000-0002-7455-1345Jinxiang Wang2https://orcid.org/0000-0003-4763-7896Department of Computer Science and Technology, Yanbian University, Yanji, ChinaDepartment of Computer Science and Technology, Yanbian University, Yanji, ChinaDepartment of Computer Science and Technology, Yanbian University, Yanji, ChinaAlthough the existing localization and mapping (SLAM) technology of indoor mobile robot has made great development, its intelligence and environmental perception ability still cannot meet the needs of service and inspection. Therefore, based on edge computing environment, a 3D semantic map construction of mobile robot based on improved ORB-SALM2 is proposed. Firstly, the improved yolov3 algorithm is used to detect indoor objects, and then the real-time semantic segmentation network model based on deep learning is used to segment indoor objects to achieve the classification of pixel points of objects on two-dimensional images, and BAFF feature fusion algorithm is introduced to improve the accuracy of semantic segmentation model. Then, through the SLAM system, we estimate the pose of the image in the result of semantic segmentation, and use the depth information to project it into the three-dimensional environment to build the three-dimensional semantic map. Finally, the experiment platform of mobile robot is built to verify the stability of ORB-D and thermal imaging sensor registration technology, the accuracy and real-time of building three-dimensional environment thermal field map, and the accuracy of robot positioning using thermal infrared and depth image.https://ieeexplore.ieee.org/document/9047931/Mobile robotedge computing3D semantic mapimproved ORB-SALM2pose estimationimage semantic segmentation |
| spellingShingle | Xu Cui Chenggang Lu Jinxiang Wang 3D Semantic Map Construction Using Improved ORB-SLAM2 for Mobile Robot in Edge Computing Environment IEEE Access Mobile robot edge computing 3D semantic map improved ORB-SALM2 pose estimation image semantic segmentation |
| title | 3D Semantic Map Construction Using Improved ORB-SLAM2 for Mobile Robot in Edge Computing Environment |
| title_full | 3D Semantic Map Construction Using Improved ORB-SLAM2 for Mobile Robot in Edge Computing Environment |
| title_fullStr | 3D Semantic Map Construction Using Improved ORB-SLAM2 for Mobile Robot in Edge Computing Environment |
| title_full_unstemmed | 3D Semantic Map Construction Using Improved ORB-SLAM2 for Mobile Robot in Edge Computing Environment |
| title_short | 3D Semantic Map Construction Using Improved ORB-SLAM2 for Mobile Robot in Edge Computing Environment |
| title_sort | 3d semantic map construction using improved orb slam2 for mobile robot in edge computing environment |
| topic | Mobile robot edge computing 3D semantic map improved ORB-SALM2 pose estimation image semantic segmentation |
| url | https://ieeexplore.ieee.org/document/9047931/ |
| work_keys_str_mv | AT xucui 3dsemanticmapconstructionusingimprovedorbslam2formobilerobotinedgecomputingenvironment AT chengganglu 3dsemanticmapconstructionusingimprovedorbslam2formobilerobotinedgecomputingenvironment AT jinxiangwang 3dsemanticmapconstructionusingimprovedorbslam2formobilerobotinedgecomputingenvironment |