Acquisition Method of Micro-road Network Map Data Based on Road Marking Points and Road Attributes

Abstract

The invention relates to a method for collecting and processing map data of a micro-road network by using a portable data collection intelligent terminal carried by a bicycle or a power-assisted vehicle. The intelligent terminal is in transmission connection with a server arranged on a nearby map collection vehicle device. According to the method, the data collection efficiency of a scene where amotor vehicle cannot collect data can be improved, and the labor intensity of a data collector can be reduced, so that micro-road network update data required by a high-precision map is provided efficiently and accurately at low cost; and particularly, a more reliable data basis is provided for map path selection in a riding or walking mode.

Description

technical field [0001] The invention relates to the technical field of map data collection, in particular to a method and system for collecting and processing map data of an urban micro-road network of road marking points and road attributes. Background technique [0002] At present, with the popularization of the application of electronic maps, more and more people download electronic maps to mobile terminals such as mobile phones for travel route planning. Even for familiar roads, they will open electronic maps for navigation planning before travel. Avoid congested roads or choose a closer road. With the development of map accuracy, in addition to public transportation and motor vehicle navigation, more and more electronic map suppliers have begun to provide navigation planning in multiple modes, such as walking mode, cycling mode and even electric vehicle and motorcycle mode. [0003] However, the disadvantage is that the maps used in different modes are basically the sa...

AI Technical Summary

Problems solved by technology

[0016] The data acquisition of the above-mentioned prior art has the following problems: 1) Multiple sensors are required for precise positioning of the vehicle, which is costly and is mainly suitable for self-driving vehicles, and has poor portability; 2) The data update efficiency is low, and the dynamics of the actual road cannot be reflected in time 3) There are many information interference items, and it is impossible to obtain accurate and comprehensive information of marking points on both sides of the road

For example, due to the existence of obstacles such as road greening trees, utility poles, and advertisements, it constitutes a serious interference to the image acquisition and laser point cloud acquisition of the acquisition vehicle, and the obtained data is noisy and poor in accuracy; and complex algorithms are required. The processing efficiency is low; 4) The road attribute factors in the riding or walking mode are not considered, which is not conducive to the planning of riding or walking paths

[0017] Therefore, due to the different requirements for map content and accuracy in different modes, there are many defects in the existing urban map data collection and update, especially the data collection involving the micro-road network, which needs to further refine and improve the road information collection method