Abstract
Simultaneous localization and mapping (SLAM) is considered to be the basic ability of intelligent mobile robots. In the past few decades, thanks to community’s continuous and in-depth research on SLAM algorithms, the current SLAM algorithms have achieved good performance. But there are still some problems. For example, most SLAM algorithms have the assumption of a static environment, but in real life, most of the environment contains moving objects, so how to deal with the moving objects in the environment requires careful consideration. What’s more, traditional geometric maps cannot specific environmental semantic information for mobile robots, so how to make robots truly understand the surrounding environment to complete some advanced tasks is also a difficult problem. In this paper, we design a scheme to improve the accuracy and robustness of SLAM in a dynamic environment. And we realize the perception of semantic information of objects in the environment through the object detection algorithm of deep learning neural network.
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Bai, N., Ma, T., Shi, W., Wang, L. (2021). Research on Semantic Vision SLAM Towards Dynamic Environment. In: Wu, X., Wu, K., Wang, C. (eds) Quality, Reliability, Security and Robustness in Heterogeneous Systems. QShine 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 381. Springer, Cham. https://doi.org/10.1007/978-3-030-77569-8_7
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