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Optimized segmentation with image inpainting for semantic mapping in dynamic scenes

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Abstract

Moving objects will obscure static objects in a dynamic scene. When the existing semantic segmentation methods deal with these static objects, there are often missing or errors in segmentation results. To solve this problem, we propose a framework that combines image inpainting and semantic segmentation, termed SIS. Our framework adds an image inpainting network and an identical semantic segmentation network in series following an original semantic segmentation network, which can make full use of the two semantic segmentation results to obtain the optimized semantic segmentation results in this scene. Moreover, we combined our framework with Simultaneous Localization and Mapping (SLAM), and conducted experiments on the TUM RGB-D dataset. Experimental results show, the combined SLAM system can construct a semantic octree map with more complete and stable semantic information in dynamic scenes.

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Acknowledgements

We acknowledge the support of the National Key Research and De-velopment Program of China under Grant (2018YFB1305001), Wuhan Science and Technology Planning Application Foundation Frontier Project (No.2019010701011413) and Open Fund of Hubei Luojia Laboratory.

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Authors and Affiliations

  1. School of Computer Science, Wuhan University, Wuhan, China

    Jianfeng Zhang

  2. Global Navigation Satellite System Research Center, Wuhan University, Wuhan, China

    Yang Liu, Chi Guo & Jiao Zhan

  3. Intelligence Institute of Wuhan University, Wuhan University, Wuhan, China

    Chi Guo

  4. Hubei Luojia Laboratory, Wuhan, China

    Chi Guo

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  1. Jianfeng Zhang
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  2. Yang Liu
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  3. Chi Guo
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  4. Jiao Zhan
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Correspondence to Chi Guo.

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Zhang, J., Liu, Y., Guo, C. et al. Optimized segmentation with image inpainting for semantic mapping in dynamic scenes. Appl Intell 53, 2173–2188 (2023). https://doi.org/10.1007/s10489-022-03487-3

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