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Open AccessArticle

A Computationally Efficient Semantic SLAM Solution for Dynamic Scenes

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
School of Economics and Management, Hubei University of Technology, Wuhan 430079, China
Institute of Surveying and Mapping, Information Engineering University, Zhengzhou 450000, China
Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(11), 1363;
Received: 27 April 2019 / Revised: 31 May 2019 / Accepted: 4 June 2019 / Published: 6 June 2019
In various dynamic scenes, there are moveable objects such as pedestrians, which may challenge simultaneous localization and mapping (SLAM) algorithms. Consequently, the localization accuracy may be degraded, and a moving object may negatively impact the constructed maps. Maps that contain semantic information of dynamic objects impart humans or robots with the ability to semantically understand the environment, and they are critical for various intelligent systems and location-based services. In this study, we developed a computationally efficient SLAM solution that is able to accomplish three tasks in real time: (1) complete localization without accuracy loss due to the existence of dynamic objects and generate a static map that does not contain moving objects, (2) extract semantic information of dynamic objects through a computionally efficient approach, and (3) eventually generate semantic maps, which overlay semantic objects on static maps. The proposed semantic SLAM solution was evaluated through four different experiments on two data sets, respectively verifying the tracking accuracy, computational efficiency, and the quality of the generated static maps and semantic maps. The results show that the proposed SLAM solution is computationally efficient by reducing the time consumption for building maps by 2/3; moreover, the relative localization accuracy is improved, with a translational error of only 0.028 m, and is not degraded by dynamic objects. Finally, the proposed solution generates static maps of a dynamic scene without moving objects and semantic maps with high-precision semantic information of specific objects. View Full-Text
Keywords: visual SLAM; indoor positioning; 3D reconstruction; semantic map; multi-sensor integrated positioning visual SLAM; indoor positioning; 3D reconstruction; semantic map; multi-sensor integrated positioning
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MDPI and ACS Style

Wang, Z.; Zhang, Q.; Li, J.; Zhang, S.; Liu, J. A Computationally Efficient Semantic SLAM Solution for Dynamic Scenes. Remote Sens. 2019, 11, 1363.

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