Journal
IEEE ACCESS
Volume 8, Issue -, Pages 214685-214695Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.3041038
Keywords
Simultaneous localization and mapping; Semantics; Feature extraction; Vehicle dynamics; Real-time systems; Cameras; Instruction sets; PSPNet-SLAM; dynamic; semantic; OCMulti-view geometry
Categories
Funding
- National Natural Science Foundation of China [51805312]
- Shanghai Sailing Program [18YF1409400]
- Training and Funding Program of Shanghai College young teachers [ZZGCD15102]
- Scientific Research Project of Shanghai University of Engineering Science [2016-19]
- Science and Technology Commission of Shanghai Municipality [19030501100]
- Shanghai University of Engineering Science Innovation Fund for Graduate Students [18KY0613]
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Simultaneous Localization and Mapping (SLAM) plays an important role in the computer vision and robotic field. The traditional SLAM framework adopts a strong static world assumption for convenience of analysis. It is very essential to know how to deal with the dynamic environment in the entire industry with widespread attention. Faced with these challenges, researchers consider introducing semantic information to collaboratively solve dynamic objects in the scene. So, in this paper, we proposed a PSPNet-SLAM: Pyramid Scene Parsing Network SLAM, which integrated the Semantic thread of pyramid structure and geometric threads of reverse ant colony search strategy into ORB-SLAM2. In the proposed system, a pyramid-structured PSPNet was used for semantic thread to segment dynamic objects in combination with context information. In the geometric thread, we proposed a OCMulti-View Geometry thread. On the one hand, the optimal error compensation homography matrix was designed to improve the accuracy of dynamic point detection. On the other hand, we came up with a reverse ant colony collection strategy to enhance the real-time performance of the system and reduce its time consumption during the detection of dynamic objects. We have evaluated our SLAM in public data sheets and real-time world and compared it with ORB-SLAM2, DynaSLAM. Many improvements have been achieved in this system including location accuracy in high-dynamic scenarios, which also outperformed the other four state-of-the-art SLAM systems coping with the dynamic environments. The real-time performance has been delivered, compared with the geometric thread of the excellent DynaSALM system.
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