Research Article
Research on Semantic Vision SLAM Towards Dynamic Environment
@INPROCEEDINGS{10.1007/978-3-030-77569-8_7, author={Nanyang Bai and Tianji Ma and Wentao Shi and Lutao Wang}, title={Research on Semantic Vision SLAM Towards Dynamic Environment}, proceedings={Quality, Reliability, Security and Robustness in Heterogeneous Systems. 16th EAI International Conference, QShine 2020, Virtual Event, November 29--30, 2020, Proceedings}, proceedings_a={QSHINE}, year={2021}, month={6}, keywords={SLAM Semantic recognition Semantic map Dynamic target detection}, doi={10.1007/978-3-030-77569-8_7} }- Nanyang Bai
Tianji Ma
Wentao Shi
Lutao Wang
Year: 2021
Research on Semantic Vision SLAM Towards Dynamic Environment
QSHINE
Springer
DOI: 10.1007/978-3-030-77569-8_7
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.
