Hierarchical Grid Map for Indoor Localization
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Solution Overview
Problem
Conventional indoor navigation systems face challenges in accurately localizing mobile devices within indoor spaces due to the limitations of satellite-based GPS and inadequate representation of indoor spaces using linear walking paths, leading to poor localization performance and inefficiencies in providing location-based services.
Innovation Solution
A multi-layered, hierarchical grid structure using polygonal tiles that tessellate the indoor space, allowing for georeferencing of physical entities and assignment of absolute probability values to each tile, enabling effective spatial indexing and improved scalability and accuracy in localization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If satellite-based GPS is used for indoor positioning, then outdoor location accuracy is maintained, but indoor positioning reliability deteriorates due to blocked line-of-sight to satellites
Solution Approach 1:
The patent introduces map constraints (walls, obstacles, navigable paths) as an intermediary between the positioning system and the device. These constraints act as a mediator that guides and corrects positioning estimates, allowing the system to maintain accuracy indoors by referencing known environmental structures rather than relying on satellite signals that cannot penetrate buildings.
Solution Approach 2:
The patent replaces the satellite-based mechanical positioning system (GPS) with an inertial sensor-based system that uses map constraints for correction. This substitution enables positioning to function indoors by using local sensors (accelerometers, gyroscopes) and environmental references instead of distant satellite signals.
2Device complexity
If linear walking paths are used to represent indoor spaces, then device complexity is reduced, but measurement precision of localization deteriorates due to inadequate space representation
Solution Approach 1:
The patent segments the continuous indoor space into discrete map constraints (walls, obstacles, navigable regions). This segmentation transforms the complex continuous environment into manageable discrete elements that can be efficiently processed while still providing sufficient detail for accurate localization.
Solution Approach 2:
The patent changes the representation parameter from simple linear paths to comprehensive map constraints that include walls, obstacles, and navigable areas. This parameter change enriches the spatial model without significantly increasing device complexity, thereby improving localization accuracy while maintaining system simplicity.
3Measurement precision
If high-resolution spatial data is used for indoor localization, then measurement precision improves, but computational intensity increases
Solution Approach 1:
The patent extracts only the essential spatial features (map constraints such as walls and obstacles) from the complete high-resolution spatial data. By taking out only the critical elements needed for localization rather than processing all detailed spatial information, the system achieves good accuracy with reduced computational energy consumption.
Data Source
AI summary
A method and a device for localization using grid-based localization of map constraints are described. In an example, an indoor space is divided into a grid of tessellated polygonal tiles in a hierarchical structure. The grid is correlated with physical entities in the indoor space by associating each physical entity with a polygonal tile. Further, an absolute probability value indicative of presence of a device therein is associated with each polygonal tile. As part of associating, the absolute probability value is allocated to each polygonal tile in each hierarchical level to create a probability map for the indoor space. The probability map is used to generate a grid map for the indoor space and the grid map is usable to determine location of the device in the indoor space.