AR Device Pairing with Monocular Depth and SLAM Alignment
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Solution Overview
Problem
Existing AR devices face challenges in efficiently aligning coordinate systems due to computational resource constraints and privacy concerns with conventional depth sensors, leading to inefficient resource management and synchronization issues.
Innovation Solution
A system using single-view depth predictions from monocular cameras and SLAM systems to align coordinate systems between AR devices, reducing the need for high computational resources by predicting depth from sparse 3D points and reconstructing dense point clouds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional depth sensors are used to align coordinate systems, then measurement precision is improved, but device complexity and power consumption increase
Solution Approach 1:
The patent replaces physical depth sensors (mechanical/optical systems) with a computational approach using monocular camera images and SLAM algorithms to predict depth and align coordinate systems. This substitution reduces hardware complexity while maintaining alignment precision through software-based depth estimation.
2Measurement precision
If conventional depth sensors are used to align coordinate systems, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The patent replaces power-intensive depth sensors with energy-efficient monocular camera imaging combined with computational SLAM and depth prediction algorithms, significantly reducing power consumption while maintaining coordinate alignment precision.
Solution Approach 2:
The patent creates a virtual depth model through image processing and SLAM algorithms that copies the depth information function without requiring physical depth sensing hardware, thereby reducing energy consumption.
3Reliability
If pre-mapping or markers are used to enable shared AR experiences, then reliability is improved, but device complexity and setup time increase
Solution Approach 1:
The patent enables the AR system to automatically establish shared coordinate frames through real-time image processing and SLAM between devices, eliminating the need for manual pre-mapping or marker placement. The system self-configures by detecting and aligning on environmental features autonomously.
Solution Approach 2:
The patent performs coordinate system alignment dynamically during device pairing and AR session initialization, eliminating the need for advance pre-mapping of environments or placement of markers before AR experiences begin.
4Measurement precision
If computational resources are increased to improve coordinate system alignment, then measurement precision is improved, but productivity decreases due to resource constraints
Solution Approach 1:
The patent uses monocular camera images and sparse SLAM points to predict depth for coordinate alignment without requiring full 3D environmental mapping or exhaustive computational processing, achieving sufficient alignment precision with partial computational effort.
Data Source
AI summary
A method for aligning coordinate systems from separate augmented reality (AR) devices is described. In one aspect, the method includes generating predicted depths of a first point cloud by applying a pre-trained model to a first single image generated by a first monocular camera of a first augmented reality (AR) device, and first sparse 3D points generated by a first SLAM system at the first AR device, generating predicted depths of a second point cloud by applying the pre-trained model to a second single image generated by a second monocular camera of the second AR device, and second sparse 3D points generated by a second SLAM system at the second AR device, determining a relative pose between the first AR device and the second AR device by registering the first point cloud with the second point cloud.