Real-Time Map Compression Method Based on Boolean Operation and Moore-Neighborhood Search.

Presently, the topics of sensor network information sharing and multi-robot mapping in robotics have gained considerable research interest. This paper introduces a real-time map compression methodology developed to address the time-intensive problem associated with large-scale map cross-system transmission. This method employs neighborhood filtering, a modified Moore-Neighborhood tracking algorithm, and polygonal Boolean operation technology to extract the inner and outer boundaries of a 2D raster map, thereby enabling map reconstruction. The proposed methodology specifically focuses on the removal of burrs from the outer boundary and reoptimization of the inner boundary search process. This is done to enhance the efficacy of the map reconstruction. Additionally, the extracted inner and outer boundaries are utilized for the reconstruction of the map. The performance of the proposed algorithm was evaluated using several types of maps. The results indicate a relatively low execution time of 300 ms for a map comprising 2 million pixels, which essentially fulfills the real-time requirements for large-scale map transmission and reconstruction. Furthermore, the quality of the map reconstruction was assessed using the Universal Image Quality Index (UIQI), Multi-Scale Structural Similarity (MS-SSIM), and Peak signal-to-noise ratio (PSNR) image evaluation metrics. The reconstruction results reveal that the map effectively satisfies the requirements with a UIQI exceeding 0.9984, an MS-SSIM surpassing 0.9282, and a PSNR above 27.7089. Simultaneously, the map’s compression rate exceeded 99.0%, thereby demonstrating that the algorithm can accomplish a significantly high compression rate. This evidence suggests the algorithm’s potential for effective deployment in real-world applications requiring large-scale map transmission and reconstruction.