Research on Image Processing and Vectorization Storage Based on Garage Electronic Maps

TL;DR

This paper presents a novel method for vectorization and storage of indoor garage maps, improving data accuracy, storage efficiency, and navigation capabilities through raster-to-vector conversion and classification techniques.

Contribution

It introduces a new classification storage method for indoor maps that enhances data organization and retrieval in electronic map systems.

Findings

Effective vectorization of garage maps achieved

Improved storage and query efficiency demonstrated

Navigation accuracy validated through testing

Abstract

For the purpose of achieving a more precise definition and data analysis of images, this study conducted a research on vectorization and rasterization storage of electronic maps, focusing on a large underground parking garage map. During the research, image processing, vectorization and rasterization storage were performed. The paper proposed a method for the vectorization classification storage of indoor two-dimensional map raster data. This method involves converting raster data into vector data and classifying elements such as parking spaces, pathways, and obstacles based on their coordinate positions with the grid indexing method, thereby facilitating efficient storage and rapid querying of indoor maps. Additionally, interpolation algorithms were employed to extract vector data and convert it into raster data. Navigation testing was conducted to validate the accuracy and reliability of the map model under this method, providing effective technical support for the digital storage and navigation of garage maps.