Map Data Update via Camera Image Processing
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Solution Overview
Problem
Map databases become outdated quickly due to changes in geographical landscapes and lack of detailed data for remote or uninhabited areas, requiring frequent updates that are resource-intensive and time-consuming.
Innovation Solution
Mobile devices equipped with cameras and GPS can capture images of new objects, determine their location, and create new map data to update the database, allowing users to contribute to map updates and improve coverage in unmapped areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional satellite or aerial imagery is used to update map databases, then comprehensive map data can be obtained, but the process is time-consuming and resource-intensive
Solution Approach 1:
The system enables users to autonomously contribute map data by capturing images with their mobile devices and automatically processing these images through computer vision algorithms to extract and submit new map features, eliminating the need for professional data collection teams to physically visit every location
Solution Approach 2:
The patent replaces traditional mechanical data collection methods (satellite imagery, aerial photography, ground-based vehicles) with automated image processing systems that use computer vision and machine learning to extract map data from user-captured photographs, significantly reducing the time and resources required for map updates
2Measurement precision
If ground-based data collection vehicles are deployed to update maps, then detailed local data can be collected, but the cost and complexity increase significantly
Solution Approach 1:
The system leverages mobile devices that users already possess, which serve multiple functions including communication, navigation, and now map data collection. This eliminates the need for specialized data collection vehicles and equipment, reducing system complexity while maintaining data collection capabilities
Solution Approach 2:
Instead of deploying expensive specialized equipment, the system uses copies of commercially available mobile devices that users already own. These devices capture images that are then processed to extract map data, providing detailed local information without requiring complex dedicated infrastructure
3Reliability
If manual verification and processing of user-submitted images is performed, then data accuracy can be maintained, but the processing time and computational resources increase
Solution Approach 1:
The system replaces manual verification processes with automated computer vision algorithms and machine learning models that can process images rapidly and accurately identify map features, building materials, and environmental conditions without human intervention
Solution Approach 2:
The image processing system operates continuously and automatically, processing user-submitted images in real-time or near-real-time as they are uploaded. This continuous automated processing maintains data accuracy while maximizing processing throughput without requiring batch manual review
Data Source
AI summary
A method of updating map data entails capturing an image using a camera, determining a location of an object in the image, creating new map data to represent the object in the image, and updating a map database to include the new map data for the object in the image. This method may be implemented on a GPS-enabled wireless communications device having an onboard camera which can transmit the new map data to a map server for updating its map database. Determining the position of the object in the image relative to the wireless device may be accomplished using a rangefinder and compass, triangulation of multiple images of the object, or a stereoscopic camera. The accuracy of the GPS position fix may be improved by capturing images of recognizable objects for which location coordinates are available.