Base Station Landscape Models for AR Offloading
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Solution Overview
Problem
Augmented reality (AR) applications on portable devices are computationally intensive, leading to resource overload and limited compatibility with various devices, and struggle with identifying changing landscapes, which hinders widespread deployment due to difficulties in recognizing seasonal or weather variations in natural environments.
Innovation Solution
A system with regional data stores at base stations, where landscape images are gathered, analyzed, and stored to create landscape models, allowing mobile access terminals to request and receive AR content based on identified landscape features, reducing computational burden on devices and enhancing compatibility across different hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AR processing is performed on portable devices, then AR functionality is achieved, but device resources are overwhelmed and compatibility is limited
Solution Approach 1:
The patent introduces base stations as intermediary components between mobile devices and the network. These base stations perform the computationally intensive AR processing tasks, including landscape model generation from crowd-sourced images and real-time AR content generation, thereby offloading the computational burden from portable devices and enabling broader device compatibility
Solution Approach 2:
The system segments the AR processing functionality by separating data collection (performed by mobile devices), data storage and model generation (performed by base stations), and AR content delivery (performed by base stations). This segmentation allows each component to have optimized resource requirements, with mobile devices needing only basic imaging and networking capabilities
2Speed
If landscape identification is performed in real-time on mobile devices, then responsive AR is achieved, but computational resources are overwhelmed
Solution Approach 1:
The system performs preliminary actions by pre-generating landscape models from crowd-sourced images at base stations before they are needed for AR operations. These models are stored and ready for rapid retrieval and processing, enabling fast AR response times without requiring mobile devices to perform computationally intensive image analysis in real-time
Solution Approach 2:
The system implements self-service through crowd-sourced image collection, where users automatically contribute landscape images during normal device operation. This crowd-sourced data is processed at base stations to build comprehensive landscape models, eliminating the need for each device to independently perform resource-intensive environmental scanning and analysis
Data Source
AI summary
A method. The method comprises maintaining a regional data store of landscape models for each of a plurality of base stations, wherein each regional data store is proximate to its corresponding base station and wherein each landscape model is derived from a plurality of images of a landscape located proximate to the base station. The method further comprises receiving a request for a landscape image from a mobile access terminal in the serving area of a first base station, wherein the first base station is one of the plurality of base stations and wherein the request for the landscape image identifies a landscape model maintained by the first base station. The method further comprises transmitting the landscape image to the mobile access terminal by the first base station, wherein the landscape image is created based on the landscape model identified in the request.