Distributed Asynchronous Localization for AR Mobile Devices
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Solution Overview
Problem
Existing augmented reality systems face challenges in efficiently decoupling environmental mapping from localization processes, particularly on mobile devices constrained by form factor and battery life, which limits their ability to handle complex interactions between real and virtual objects.
Innovation Solution
A distributed asynchronous approach where independent sensing devices handle mapping and mobile devices utilize separate asynchronous computing pipelines for localization and rendering, allowing for efficient mapping and localization processes, even for a large number of mobile devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional SLAM techniques are used to perform simultaneous localization and mapping on mobile devices, then the system can build a map of the environment and track location, but the device complexity and computational resource requirements increase significantly
Solution Approach 1:
The patent divides the SLAM system into two separate components: a mapping system that performs environmental mapping using independent sensing devices, and mobile devices that perform only localization using pre-acquired map data. This segmentation allows computationally intensive mapping operations to be performed by a dedicated system while mobile devices focus on lighter localization tasks, reducing their computational complexity and power consumption.
Solution Approach 2:
The patent introduces a pre-acquired map of the environment as an intermediary data structure that mediates between the mapping system and mobile devices. The map serves as a shared reference that enables mobile devices to perform localization without needing to perform full SLAM computations, thereby reducing device complexity while maintaining localization accuracy.
2Adaptability or versatility
If multiple mobile devices perform full SLAM computations independently, then each device can operate autonomously, but the energy consumption and battery life are significantly reduced
Solution Approach 1:
The patent segments the computational workload by separating mapping functions (performed by a dedicated mapping system) from localization functions (performed by mobile devices). This allows mobile devices to maintain autonomy in localization while consuming significantly less power by not performing full SLAM computations independently.
Solution Approach 2:
The patent performs the computationally intensive mapping action in advance using a dedicated mapping system, producing pre-acquired map data that mobile devices can then use for localization. This preliminary action eliminates the need for mobile devices to perform repeated full SLAM computations, thereby reducing their power consumption while maintaining operational autonomy.
3Productivity
If sparse point cloud maps are used for camera localization, then the localization process is computationally efficient, but complex augmented reality applications cannot handle collisions and occlusions
Solution Approach 1:
The patent applies different levels of map detail to different functional requirements: sparse point cloud maps are used for the localization function where computational efficiency is critical, while dense maps are used for augmented reality rendering functions where handling collisions and occlusions is critical. This local quality differentiation allows the system to optimize for both processing speed and AR application capability.
Data Source
AI summary
A system and method for providing an augmented reality environment in which the environmental mapping process is decoupled from the localization processes performed by one or more mobile devices is described. In some embodiments, an augmented reality system includes a mapping system with independent sensing devices for mapping a particular real-world environment and one or more mobile devices. Each of the one or more mobile devices utilizes a separate asynchronous computing pipeline for localizing the mobile device and rendering virtual objects from a point of view of the mobile device. This distributed approach provides an efficient way for supporting mapping and localization processes for a large number of mobile devices, which are typically constrained by form factor and battery life limitations.