Abstract
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.
Highlights
Simultaneous Localization and Mapping (SLAM) is a cutting-edge relevant technology in the field of robot movement
The field of visual SLAM has attracted a large number of researchers with emergence of many excellent SLAM system frameworks such as MonoSLAM[1], ORB-SLAM[2], ORB-SLAM2[3], LSD-SLAM[4], SVO[5], DynaSLAM[6], which can achieve satisfactory performance while mobile robots are used in a static environment or some dynamic elements moves in space
⚫ We proposed the algorithm framework of PSNetSLAM, and introduced the Pyramid Scene Parsing Network (PSPNet) network of the pyramid structure as a parallel semantic thread on the basis of ORB-SLAM2
Summary
SLAM is a cutting-edge relevant technology in the field of robot movement. When a robot collects data information from the surrounding environment through sensors, it uses relevant effective information to conduct self-positioning and surrounding environment map construction. The field of visual SLAM has attracted a large number of researchers with emergence of many excellent SLAM system frameworks such as MonoSLAM[1], ORB-SLAM[2], ORB-SLAM2[3], LSD-SLAM[4], SVO[5], DynaSLAM[6], which can achieve satisfactory performance while mobile robots are used in a static environment or some dynamic elements moves in space These excellent SLAM systems currently perform well in ideal static environments to precisely locate and map something, them, they are still required to be test in our reality space (indoor and outdoor) where exists numerous moving objects. The dual thread collaboratively works to extract the dynamic objects in the scene, so as to improve the accuracy of the self-positioning of the SALM system with more real-time performance and more robustness of dynamic point detection.
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