Research on real-time fusion of 2D raster maps based on feature point matching

Mingxi Huang; Chao Chen; Qianli Wei

doi:10.1117/12.3033191

5 July 2024 Research on real-time fusion of 2D raster maps based on feature point matching

Mingxi Huang, Chao Chen, Qianli Wei

Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 131841T (2024) https://doi.org/10.1117/12.3033191
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

When a single mobile robot is used in SLAM construction, the low efficiency of SLAM construction will occur due to the small number of mobile robots. When multi-mobile robots are used in SLAM construction, aiming at the two-dimensional raster local environment map generated by multiple mobile robots, this paper proposes an improved real-time fusion method of two-dimensional raster map based on feature point matching. The improved algorithm PROSAC is adopted to replace RANSAC algorithm and improve algorithm efficiency to eliminate mismatched feature points. Improve the efficiency of map fusion. First, two mobile robots were used to construct the local environment map by using the Gmapping mapping algorithm respectively. Then, the constructed two-dimensional raster map was processed in gray scale, and the feature points in the raster map after gray scale processing were extracted, filtered and matched, and finally the global map fusion was completed. Through gazebo simulation experiment, local maps constructed by multiple mobile robots were fused to verify the feasibility and effectiveness of the proposed method.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mingxi Huang, Chao Chen, and Qianli Wei "Research on real-time fusion of 2D raster maps based on feature point matching", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 131841T (5 July 2024); https://doi.org/10.1117/12.3033191

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