Hierarchical Map Segmentation for Low-Memory Indoor Positioning
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Solution Overview
Problem
Existing devices for indoor positioning, such as smartphones, face inefficiencies in terms of cost, weight, and power consumption, and are impractical in environments where they cannot be carried, while embedded systems have limited battery and memory capacity, necessitating a method for low-power and low-memory indoor positioning.
Innovation Solution
A computing device executes operation modes based on target motion and movement, loads map segments from memory, and acquires positioning data using real-time sensing data, segmenting map data into predetermined sizes to minimize power and memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If an embedded type small device is used for indoor positioning, then the device can operate in environments where smartphones cannot be carried, but the battery capacity and memory capacity become limited
Solution Approach 1:
The patent segments map data into multiple map segments stored in external memory, loading only necessary segments into internal memory during operation. This segmentation allows the system to function with limited internal memory capacity while maintaining access to comprehensive positioning data through external storage.
Solution Approach 2:
The system performs preliminary actions by pre-processing and segmenting map data before operation, storing it in external memory. During actual positioning operations, only required segments are loaded into internal memory, reducing the memory capacity requirements for the embedded device.
2Measurement precision
If map data is loaded entirely into memory for indoor positioning, then positioning accuracy is maintained, but power consumption and memory usage increase
Solution Approach 1:
Map data is divided into multiple segments that are stored in external memory. Only the specific map segment corresponding to the current positioning area is loaded into internal memory and processed, significantly reducing power consumption compared to loading entire map data while maintaining positioning accuracy for the current location.
Solution Approach 2:
The system applies local quality by focusing computational resources and memory allocation on the specific map segment relevant to the current positioning area rather than processing entire map data uniformly. This localized approach maintains positioning accuracy for the current location while minimizing overall power consumption.
3Quantity of substance
If map data is segmented into small portions, then memory usage is reduced, but the complexity of managing and loading segments increases
Solution Approach 1:
The system employs feedback mechanisms where the positioning result from the current map segment determines whether to load adjacent segments for continuity. This feedback-based segment management allows the system to maintain low memory usage while dynamically adjusting segment loading based on actual positioning needs, reducing the complexity of manual segment management.
Solution Approach 2:
The map segment loading is made dynamic rather than static. The system dynamically determines which map segment to load based on real-time positioning information and movement detection, allowing flexible memory management that adapts to current operational needs without requiring complex pre-planning of segment structures.
Data Source
AI summary
Disclosed is a low power and low memory based indoor positioning method performed by a computing device including at least one processor. The method may include: determining an operation mode to be executed based on at least one of a motion of a target and movement of the target; loading a map segment related to a current location of the target from a memory when executing a specific mode related to performing indoor positioning; and acquiring positioning data by using sensing data collected in real time for the indoor positioning, and the map segment.