Zone Division for Mobile Devices Using Door Recognition
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Solution Overview
Problem
Conventional methods for dividing a closed space into zones for mobile devices are inaccurate and inefficient, as they typically expand outwardly from the room center, which does not logically follow the object's movement patterns.
Innovation Solution
A method and apparatus that process trajectory points to recognize correct doors in a closed space, allowing for zone division based on these doors, involving candidate point determination, clustering, filtering, and zone fusion to accurately define zones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If zone division is performed by expanding outwardly from the room center, then the division process is simple to implement, but the zone division accuracy is low
Solution Approach 1:
The patent applies preliminary action by performing door recognition and trajectory point processing before zone division. The system first identifies candidate points from trajectory data, clusters them to find door locations, and then uses these door positions as references for accurate zone division. This preliminary door detection step ensures that zones are divided based on actual spatial features rather than arbitrary center-based expansion, thereby improving zone division accuracy while managing algorithm complexity through a structured multi-step process.
2Productivity
If conventional center-based expansion method is used for zone division, then the implementation is straightforward, but the division efficiency is low
Solution Approach 1:
The patent improves division efficiency by performing preliminary door recognition and trajectory point clustering before the actual zone division process. By pre-identifying door locations and using them as reference points, the system can quickly divide zones without needing to perform complex iterative calculations from the room center. This preliminary processing step streamlines the overall division process, making it more efficient while maintaining implementation feasibility through a clear sequential workflow.
3Measurement precision
If trajectory points are processed through clustering and filtering to recognize doors, then the zone division accuracy is improved, but the computation time increases
Solution Approach 1:
The patent applies partial action by implementing a multi-stage filtering process that processes trajectory points incrementally. Instead of processing all trajectory points with full clustering algorithms at once, the system first identifies candidate points, then applies clustering only to these candidates, and finally filters the clustered results to identify doors. This partial processing approach reduces computation time by avoiding unnecessary processing of all trajectory points, while still achieving accurate door recognition through the focused application of clustering and filtering operations on relevant subsets of data.
Data Source
AI summary
A method includes obtaining a map of a closed space. The method also includes obtaining trajectory points. The method also includes processing the trajectory points, and recognizing a correct door in the closed space based on the map and a result of processing the trajectory points. The method also includes dividing the closed space into zones based on the correct door and the map.