International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLII-3/W10
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W10, 431–435, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W10-431-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W10, 431–435, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W10-431-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  07 Feb 2020

07 Feb 2020

RESEARCH ON SEMANTIC MAP GENERATION AND LOCATION INTELLIGENT RECOGNITION METHOD FOR SCENIC SPOT SPACE PERCEPTION

B. S. Li1,2, B. Liu1, X. S. Ni1, P. Huang1, and L. L. Pu1 B. S. Li et al.
  • 1College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, Guangxi, China
  • 2Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin, Guangxi, China

Keywords: Semantic Map, feature extraction, Feature matching, Location perception

Abstract. In recent years, Tourism has become more and more Chinese leisure travel choice The research on the smart scenic spot is getting deeper and deeper, but the problem of accurate location l in the natural scenic spot still needs to be solved. Semantic maps contain a wealth of environmental information and can be more efficient for location-aware services, and are attracting more and more attention from researchers at home and abroad. In order to better ensure the travel experience of tourists, the range of scenic spots is too large, and the signal interference is high. Complex terrain in the scenic area, Branch and leaf features Visitors cannot rely on traditional positioning systems to get their current accurate location. It is proposed to construct a navigation semantic map for the perception of scenic space. In the construction process, the operation based on the location perception of the tourists and the surrounding environment and the extraction of the feature information is the key to constructing the semantic map. The general image recognition method is used to obtain the environment image information, and the acquired feature image is recognized to obtain the semantic information in the environment; in order to obtain more feature environment information to better complete the location-aware service task, the GBP descriptor is used. The method divides and stores different semantic regions in the environment, and generates a semantic map with rich semantic information and feature information according to the three-dimensional map model.