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An Improved High Precision 3D Semantic Mapping of Indoor Scenes from RGB-D Images

Jing Xin1,*, Kenan Du1, Jiale Feng1, Mao Shan2

1 Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi’an University of Technology, Xi’an, 710048, China
2 Australian Centre for Field Robotics, The University of Sydney, Sydney, 2006, Australia

* Corresponding Author: Jing Xin. Email: email

(This article belongs to this Special Issue: Advanced Intelligent Decision and Intelligent Control with Applications in Smart City)

Computer Modeling in Engineering & Sciences 2023, 137(3), 2621-2640.


This paper proposes an improved high-precision 3D semantic mapping method for indoor scenes using RGB-D images. The current semantic mapping algorithms suffer from low semantic annotation accuracy and insufficient real-time performance. To address these issues, we first adopt the Elastic Fusion algorithm to select key frames from indoor environment image sequences captured by the Kinect sensor and construct the indoor environment space model. Then, an indoor RGB-D image semantic segmentation network is proposed, which uses multi-scale feature fusion to quickly and accurately obtain object labeling information at the pixel level of the spatial point cloud model. Finally, Bayesian updating is used to conduct incremental semantic label fusion on the established spatial point cloud model. We also employ dense conditional random fields (CRF) to optimize the 3D semantic map model, resulting in a high-precision spatial semantic map of indoor scenes. Experimental results show that the proposed semantic mapping system can process image sequences collected by RGB-D sensors in real-time and output accurate semantic segmentation results of indoor scene images and the current local spatial semantic map. Finally, it constructs a globally consistent high-precision indoor scenes 3D semantic map.


Cite This Article

Xin, J., Du, K., Feng, J., Shan, M. (2023). An Improved High Precision 3D Semantic Mapping of Indoor Scenes from RGB-D Images. CMES-Computer Modeling in Engineering & Sciences, 137(3), 2621–2640.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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