With the rapid development of the Internet, an increasing number of new industries such as "Internet Plus" intelligent transportation and unmanned systems based on location-based services have been gradually developed. The development of these industries requires the support of high precision location data, which the 5 m accuracy of traditional navigation maps cannot provide. To overcome the drawbacks of traditional maps, high precision road navigation maps have been proposed. High precision road navigation maps can provide more detailed road information and are thus able to more accurately reflect the real situation of roads. Compared to traditional maps, high precision road navigation maps possess three advantages. First, they include additional map layers. Second, the content of the layers is finer. Third, a new map structure is divided. However, the rich information content of high precision maps leads to the generation of huge amounts of data. Traditional centralized big data processing modes are unable to meet the computing needs required for processing such huge amounts of data. Therefore, in this paper, we propose a big data processing model involving "crowdsourcing + edge computing" to address the problem of high precision map calculation. At present, high precision road navigation maps have kicked into a high gear. Nevertheless, certain problems persist that need to be addressed during the process of development.