Map Data Floor Division Using Height Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing map data processing technologies face challenges in distinguishing between multiple floors, making it difficult to apply maps for accurate vehicle positioning and navigation in multi-floor environments.
Innovation Solution
A method and apparatus for data processing that determines target path point elements and map element clusters based on gradient and height information, performing floor division using average height information to differentiate between floors, and includes a height calibration module for accurate map data processing in multi-floor settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If map data is collected in real time for multi-floor locations, then the map coverage is improved, but the ability to distinguish between floors deteriorates
Solution Approach 1:
The patent introduces height information as a new dimension (z-axis) to differentiate map elements across multiple floors. By clustering map elements based on their height values, the system can distinguish between floors while maintaining comprehensive map coverage. This transforms the traditional 2D map representation into a 3D spatial structure where vertical position becomes the key discriminator for multi-floor identification.
2Measurement precision
If height information is used for floor division, then floor distinction accuracy is improved, but the complexity of data processing increases
Solution Approach 1:
The patent extracts height information as a separate, independent feature from the map data and uses it specifically for floor division. By isolating the height dimension and applying clustering algorithms only on this extracted feature, the system achieves accurate floor distinction without needing to process the entire map dataset in complex ways. This selective extraction simplifies the overall processing complexity while maintaining precision.
3Manufacturing precision
If map elements are clustered based on height information, then floor division accuracy is improved, but the computational resources required increase
Solution Approach 1:
The patent applies clustering operations locally to map elements within specific height ranges rather than processing all map elements globally. By dividing the height space into segments and performing clustering on localized groups of elements, the system achieves accurate floor division while significantly reducing the computational burden. This local processing approach maintains precision for each floor while optimizing resource utilization.
Data Source
Figure 1~2
Figure 3
Figure 4
AI summary
Embodiments of the present disclosure provide a method and an apparatus for data processing. The method includes: acquiring map data; determining, from the map data, a target map element corresponding to a flat mode; obtaining a map element cluster, by clustering a number of map elements in the map data based on the target map element; and dividing the number of map elements in the map data into a floor in accordance with the map element cluster. With the embodiments of the present disclosure, floor division based on the map elements is achieved, such that the map data can be applied to multiple floors, and practicability of the map is improved.