Journal of System Simulation
Abstract
Abstract: Semantic information perception of the external environment and accurate positioning are the keys to autonomous navigation and operation of mobile robots. This paper proposes a method of semantic simultaneous localization and mapping (SLAM) based on a monocular camera. The system completes three-dimensional (3D) object detection while estimating the trajectory. We model the 3D objects with cuboids. Then, the semantic meanings, color distribution, size and neighborhood topology of the objects are extracted as descriptors for the accurate matching of objects between different frames. The camera pose, map points and object landmarks are optimized jointly in the backend of the system. The weight coefficient of each error term in the cost function is autonomously adjusted to improve the estimation accuracy and robustness of each state variable of the system. The experimental results show that the proposed method has high accuracy in map construction.
Recommended Citation
Lin, Shiqi; Wang, Jikai; Pei, Haoyuan; Zhao, Hao; and Chen, Zonghai
(2022)
"Monocular Semantic SLAM Method Based on Object Relation Description,"
Journal of System Simulation: Vol. 34:
Iss.
2, Article 10.
DOI: 10.16182/j.issn1004731x.joss.20-0734
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol34/iss2/10
First Page
278
Revised Date
2020-12-10
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.20-0734
Last Page
284
CLC
TP391
Recommended Citation
Shiqi Lin, Jikai Wang, Haoyuan Pei, Hao Zhao, Zonghai Chen. Monocular Semantic SLAM Method Based on Object Relation Description[J]. Journal of System Simulation, 2022, 34(2): 278-284.
DOI
10.16182/j.issn1004731x.joss.20-0734
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