Jing Xin1,*, Kenan Du1, Jiale Feng1, Mao Shan2
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2621-2640, 2023, DOI:10.32604/cmes.2023.027467
- 03 August 2023
Abstract 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
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