Hierarchical Tessellated Grids for Indoor Localization Accuracy
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Solution Overview
Problem
Indoor location-based services face challenges in accurately and efficiently localizing mobile devices within enclosed spaces due to reliance on satellite-based navigation systems and the need for effective utilization of sensor and wireless communication data, leading to inefficiencies in data processing and localization accuracy.
Innovation Solution
A tessellated, hierarchical grid system is employed, where indoor spaces are divided into polygonal tiles with a hierarchical structure, allowing for efficient spatial indexing and dynamic resolution selection based on localization requirements, enabling accurate and computationally light localization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If satellite-based navigation systems are used for indoor positioning, then outdoor location accuracy is improved, but positioning reliability deteriorates in enclosed spaces due to lack of line-of-sight
Solution Approach 1:
The patent introduces an intermediary indoor positioning system that mediates between satellite-based GPS and indoor sensor data. The system uses a hybrid approach where GPS provides outdoor context and indoor sensors (accelerometers, gyroscopes, magnetometers, Wi-Fi, Bluetooth) provide indoor positioning, with the intermediary server coordinating both data sources to maintain continuous accurate positioning throughout the transition from outdoor to indoor environments.
Solution Approach 2:
The patent segments the positioning system into distinct functional components: outdoor satellite-based navigation, indoor sensor-based positioning, and a coordinating server. Each segment operates independently but interfaces with others through standardized protocols, allowing the system to switch between GPS and indoor sensors based on availability and accuracy requirements.
2Reliability
If multiple sensors and wireless communication signals are used for indoor localization, then positioning capability is improved, but data processing complexity increases
Solution Approach 1:
The patent divides the complex data processing task into segments handled by different components. The mobile device collects and pre-processes sensor data locally, while the server handles the complex fusion of multiple data sources and generates final positioning results. This segmentation reduces the processing burden on individual devices while maintaining comprehensive positioning capability.
Solution Approach 2:
The system implements feedback mechanisms where positioning results are continuously refined based on sensor data quality and availability. The server monitors the reliability of different data sources and adjusts the weighting and processing methods accordingly, automatically adapting to changing environmental conditions and data quality without requiring manual intervention.
3Measurement precision
If high-resolution spatial data is used for localization, then measurement precision is improved, but computational requirements and processing time increase
Solution Approach 1:
The patent segments the spatial data into hierarchical levels or zones, allowing the system to process only the necessary resolution required for the current positioning task. The server can select appropriate data granularity based on the user's location, movement speed, and the specific positioning requirements, avoiding unnecessary processing of high-resolution data when low-resolution approximation suffices.
Solution Approach 2:
The system dynamically adjusts the resolution and processing intensity of spatial data based on real-time conditions. When the device is stationary or moving slowly, the system uses higher-resolution data for accurate positioning. When the device moves quickly or enters dynamic environments, the system automatically reduces processing resolution to maintain responsiveness, accepting slightly lower precision in exchange for reduced processing time.
Data Source
AI summary
Examples for localization using tessellated grids are described. In an example, a plurality of physical entities can be identified in an indoor space and each physical entity can be georeferenced to a coordinate system. The indoor space can be divided into a grid of polygonal tiles abutting adjacent polygonal tiles, and arranged in a hierarchical structure with the tiles in one level of the hierarchical structure being substantially representable by polygonal tiles in other levels of the hierarchical structure. The grid is then spatially indexed by associating each physical entity with a polygonal tile from amongst the polygonal tiles in each hierarchical level, thereby correlating a georeference of the physical entity with the polygonal tile. Based on the spatial indexing, a grid map for the indoor space can be generated for localizing mobile devices in the indoor space.