Remote SLAM Offloading for Battery-Efficient Mobile AR
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Solution Overview
Problem
Low-end devices lack the computational resources to perform Simultaneous Localization And Mapping (SLAM) and Augmented Reality (AR) processing, leading to battery drain and limited functionality.
Innovation Solution
Offload SLAM computations to a remote server with advanced processing capabilities, allowing low-end devices to capture and transmit data for real-time AR capabilities without performing complex calculations locally.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If SLAM processing is performed onboard the low-end device, then spatial intelligence and AR capabilities are achieved, but battery life is significantly reduced and device functionality is limited
Solution Approach 1:
The patent extracts the computationally intensive SLAM processing function from the low-end user device and relocates it to a remote server. The user device only captures images and transmits them to the server, while the server performs all SLAM computations and returns results. This extraction resolves the contradiction by eliminating the energy-consuming onboard processing while maintaining full spatial intelligence functionality.
Solution Approach 2:
The patent introduces a remote server as an intermediary between the user device and the SLAM processing function. The server acts as a mediator that receives images from the device, performs comprehensive SLAM computations, and returns processed results. This intermediary approach allows the low-end device to access advanced spatial intelligence capabilities without bearing the computational burden, thus preserving battery life.
2Adaptability or versatility
If SLAM processing is performed onboard the low-end device, then autonomous spatial understanding is achieved, but device complexity and computational requirements increase
Solution Approach 1:
The patent extracts the complex SLAM processing algorithms and computational requirements from the user device, placing them entirely on the remote server. The device only needs basic image capture and network communication capabilities. This extraction maintains autonomous spatial understanding functionality while dramatically reducing the computational requirements and complexity of the user device.
Solution Approach 2:
The remote server provides universal SLAM processing capabilities that can serve multiple user devices simultaneously. Instead of each device needing its own dedicated computational resources for SLAM, a single multi-functional server handles processing for many devices, reducing the computational burden on individual low-end devices while maintaining full spatial understanding capabilities.
3Use of energy by moving object
If SLAM processing is offloaded to a remote server, then battery life is extended and AR capabilities are enhanced, but real-time processing latency may increase
Solution Approach 1:
The system performs preliminary actions by maintaining persistent connections and pre-establishing communication channels between the device and server. Image capture and transmission are prepared in advance, and the server maintains ready-state processing capabilities. This preliminary preparation reduces the actual processing latency when images are transmitted, mitigating the time loss from offloading while preserving battery life benefits.
Solution Approach 2:
The patent ensures continuous useful action by maintaining persistent network connections and ready-state processing on the server. Instead of establishing connections intermittently, the system keeps communication channels open and processing capabilities active, reducing connection establishment delays. This continuity minimizes the effective latency experienced by the user while the device consumes minimal energy during idle periods.
Data Source
AI summary
A request to add an overlay at a pixel located at (x, y) coordinates in images is received. Waypoints in a three-dimensional space are identified with respective two-dimensional projections onto the images falling inside a circle with a given radius r and centered at (x, y). An overlay z-coordinate is determined for the overlay as a weighted sum of respective z coordinates of the waypoints. The overlay is assigned a location (x, y, overlay z-coordinate).