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http://hdl.handle.net/10119/17589
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| Title: | Semantic Mapping Based on Image Feature Fusion in Indoor Environments |
| Authors: | Jin, Cong
Elibol, Armagan
Zhu, Pengfei
Chong, Nak-Young |
| Keywords: | Semantic mapping
Deep learning
Scene recognition
Image feature fusion |
| Issue Date: | 2021-10 |
| Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
| Magazine name: | 2021 21st International Conference on Control, Automation and Systems (ICCAS 2021) |
| Start page: | 693 |
| End page: | 698 |
| DOI: | 10.23919/ICCAS52745.2021.9650062 |
| Abstract: | It is of the utmost importance for the robot to understand human semantic instructions in human-robot interaction. Combining semantic information with SLAM-based maps leads to a semantic map. Deep neural networks are able to extract useful information from the robot’s visual information. In this paper, we integrate the RGB feature information extracted by the classification network and the detection network to improve the robot’s scene recognition ability and make the acquired semantic information more accurate. The image segmentation algorithm labels the areas of interest in the metric map. Furthermore, the fusion algorithm is incorporated to obtain the semantic information of each area, and the detection algorithm recognizes the key objects in the area. We have demonstrated an efficient combination of semantic information with the occupancy grid map toward accurate semantic mapping. |
| Rights: | This is the author's version of the work. Copyright (C) 2021 IEEE. 2021 21st International Conference on Control, Automation and Systems (ICCAS 2021), 2021, pp.693-698. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| URI: | http://hdl.handle.net/10119/17589 |
| Material Type: | author |
| Appears in Collections: | b11-1. 会議発表論文・発表資料 (Conference Papers)
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Size | Format |
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| ICCAS_2021_Jin.pdf | | 1763Kb | Adobe PDF | View/Open |
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