Client Device Pose Estimation Using a Pose Prior Model
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Solution Overview
Problem
Existing online systems lack contextual information for accurately determining the pose of client devices, leading to high uncertainty in location and orientation, particularly in augmented reality applications, due to the lack of contextual information on existing systems.
Innovation Solution
Implementing a pose prior model and a pose objective function to estimate a pose of a client device, the use of a pose prior model and a pose objective function to quantify how well a candidate pose complies with the pose prior model and the received pose data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pose data is used directly without contextual information, then the system is simple to operate, but the measurement precision of pose is poor due to high uncertainty
Solution Approach 1:
The patent introduces a pose prior model as an intermediary component that bridges the raw pose data and the final pose estimation. This model incorporates contextual information about typical device poses and uses it to refine the initial pose estimates, thereby improving measurement precision without requiring direct complex interactions between all system components.
Solution Approach 2:
The patent implements a feedback mechanism where the pose prior model continuously refines pose estimates based on the received pose data. The system iteratively adjusts the pose estimation by comparing it against the prior model's expectations, allowing the system to learn from previous patterns and improve accuracy over time while maintaining operational simplicity.
2Measurement precision
If contextual information is added to pose estimation, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing pose prior models that capture typical device poses and contextual information before actual pose estimation occurs. This allows the system to leverage accumulated contextual knowledge without adding complexity during the real-time estimation process, as the heavy lifting is done in advance during model generation.
Solution Approach 2:
The patent implements local quality by creating region-specific pose prior models that capture contextual information relevant to particular geographic or environmental areas. Instead of using a single complex global model, the system divides the space into regions and maintains localized models, reducing overall system complexity while improving precision through location-specific contextual knowledge.
Data Source
AI summary
An online system uses a pose prior model and a pose objective function to estimate the pose of a client device. A pose prior model is a model for prior information known about client devices and their poses without reference to a particular client device and its pose data. The online system receives pose data from a client device and computes an estimated pose for the client device based on the received pose data, the pose prior model, and a generated initial candidate pose for the client device. The online system uses these as inputs to a pose objective function and optimizes the pose objective function to estimate a pose for the client device. The online system transmits this estimated pose to the client device, and may use the estimated pose as the pose for the client device for the purposes of delivering content to the user.