Indoor Map Network Data Generation Using Space Segmentation
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Solution Overview
Problem
Existing technologies face challenges in efficiently generating network data for indoor maps, particularly in complex indoor spaces where attribute information is needed for accurate route search, and Delaunay triangulation generates excessive nodes, leading to longer data generation times.
Innovation Solution
A network data generation device and method that determines whether a target space is a room or a passage based on input data, generating appropriate links and nodes to efficiently create network data for indoor maps, including attribute information for navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If Delaunay triangulation is performed to generate network data from indoor space shape, then network data can be automatically generated, but unnecessary nodes are excessively generated and data generation time increases
Solution Approach 1:
The patent segments the indoor space into distinct semantic regions (rooms and passages) and applies different network data generation rules to each segment. Rooms are represented as nodes with attribute information, while passages are represented as links connecting nodes. This segmentation prevents unnecessary nodes from being generated throughout the entire space, thereby reducing data generation time while maintaining automation.
2Ease of manufacture
If attribute information is not allocated beforehand during network data generation, then generation process is simpler, but attribute information must be associated with network data later increasing complexity
Solution Approach 1:
The patent performs preliminary action by extracting and allocating attribute information (such as width, depth, height, floor material, entrance dimensions, and staircase characteristics) during the network data generation process itself. The determination unit identifies semantic regions and the link/node generation unit creates network data with embedded attribute information, eliminating the need for subsequent association steps and reducing overall process complexity.
3Productivity
If network data is generated without classifying spaces into rooms and passages, then generation is faster, but connection of upper and lower floors becomes complex requiring separate association
Solution Approach 1:
The patent segments the indoor space into vertically distinct regions (upper floors and lower floors) and generates network data that explicitly represents vertical connections. The determination unit identifies semantic regions across different floors, and the link/node generation unit creates network data that includes vertical linkage information, enabling automatic connection of upper and lower floors without requiring separate association processes.
Data Source
AI summary
Appropriate network data for an indoor map can be efficiently generated using input data including a structure of an indoor space. In a network data generation device (10) that generates, from the input data including at least the structure of the indoor space and information indicating a property based on the structure of the indoor space, network data, the network data including a link representing a movable space on a map and a node that is a starting point or an ending point of the link, a link/node generation unit (142) generates a set of links and a set of nodes based on the input data.