Private Space Mapping via Mobile Sensor Data Fusion
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Solution Overview
Problem
Current online mapping services face challenges in efficiently generating and updating maps of private spaces, as they rely on centralized data collection and require physical roaming with sensors, making it difficult to adapt to changes without coordination between third parties and centralized services.
Innovation Solution
Utilizing mobile computing device sensors to gather data and scheduling information, which is then processed to generate digital maps of private spaces, enhancing public maps with private space information while ensuring privacy by not sharing this data with public mapping services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If centralized service collects data at multiple locations to generate maps, then map coverage is improved, but operational complexity and time consumption increase
Solution Approach 1:
The system enables users to automatically contribute location data through their mobile devices without requiring centralized coordination. Each mobile device independently collects and transmits data, eliminating the need for organized physical roaming teams while expanding map coverage through distributed user participation.
Solution Approach 2:
The centralized mapping task is divided into individual data collection units performed by separate mobile devices. Each device independently captures location information and transmits it to the server, allowing parallel data collection across multiple locations simultaneously, thus reducing total data collection time while expanding coverage.
2Measurement precision
If physical roaming with sensors is performed to update maps, then measurement precision is improved, but device complexity and operational difficulty increase
Solution Approach 1:
Standard mobile devices with built-in sensors serve multiple purposes: they function as both everyday communication tools and precision measurement instruments for map generation. This eliminates the need for specialized sensor equipment and complex deployment procedures, as any mobile device can contribute location data with sufficient accuracy.
Solution Approach 2:
Instead of deploying physical sensor arrays, the system uses digital copies of location data from mobile devices. The sensor data is captured, transmitted as digital information, and processed by the server to generate and update maps, replacing complex physical measurement setups with simplified digital data collection.
3Reliability
If centralized service coordinates with third parties to update maps, then map reliability is improved, but adaptability to changes deteriorates
Solution Approach 1:
The system establishes continuous feedback loops where mobile devices constantly report location data to the server. When changes occur in physical spaces, users automatically detect and report them through ongoing data collection, enabling the map to adapt in real-time without requiring coordinated updates between third parties and centralized services.
Solution Approach 2:
The system proactively collects location data before changes become significant issues. By continuously monitoring positions and comparing them against existing map data, the system detects changes early and updates maps preventively, maintaining reliability while rapidly adapting to new conditions without waiting for formal coordination cycles.
Data Source
AI summary
Digital maps of private spaces may be implemented using mobile computing device sensors. Sensor data may be received from one or more mobile computing devices to determine a digital signature describing a private space. Scheduling data may also be received from the one or more mobile devices. The scheduling data may describe a location associated with the private space to be mapped. A digital map of the private space may then be generated from the digital signature and the location associated with the private space in the scheduling data.