Sensor Fusion for User Position Detection
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Solution Overview
Problem
Existing computing devices face challenges in accurately determining user position and orientation relative to the device, particularly due to the limitations of either image-based tracking, which is computationally costly and latency-prone, or inertial sensor-based motion detection, which can drift over time, leading to unreliable user interface rendering.
Innovation Solution
Combining image-based tracking and inertial sensor-based motion detection through sensor fusion techniques, such as Kalman filtering, to aggregate data from multiple sensors like cameras, accelerometers, and gyroscopes, allowing for more robust and accurate user position estimation by weighting reliability scores from each method.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image-based tracking is used to determine user position, then measurement precision is improved, but device complexity and processing time increase
Solution Approach 1:
The patent combines image-based tracking data with inertial sensor data through sensor fusion techniques. The head tracking system processes camera images to determine head position, while inertial sensors independently track device motion. These two data sources are merged to produce a composite head position estimate, leveraging the strengths of both methods while compensating for their individual weaknesses.
Solution Approach 2:
The patent introduces an intermediary processing system that acts as a mediator between the head tracking system and the motion tracking system. This intermediary fuses the data from both sources and produces a unified head position estimate, allowing the system to benefit from both image-based precision and inertial sensor responsiveness without requiring direct integration of the two complex subsystems.
2Speed
If inertial sensor-based motion detection is used to track device movement, then speed of detection is improved, but reliability deteriorates due to drift over time
Solution Approach 1:
The patent implements a feedback mechanism where the head tracking system continuously monitors the actual head position and provides correction signals to the motion tracking system. When drift occurs in the inertial sensor-based motion tracking, the feedback from the image-based head tracking system detects the discrepancy and adjusts the composite head position estimate accordingly, preventing cumulative error from degrading reliability.
Solution Approach 2:
The patent merges the fast but drift-prone inertial sensor data with the slower but more reliable image-based head tracking data. By combining these two data sources through sensor fusion, the system achieves both the speed of inertial detection and the long-term reliability of visual tracking, as each method compensates for the other's deficiencies.
3Reliability
If multiple sensors are combined through sensor fusion, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent segments the sensor fusion process into distinct functional modules: a head tracking system that processes camera images, a motion tracking system that processes inertial sensor data, and a data fusion module that combines these inputs. This segmentation allows each subsystem to be optimized and processed independently, reducing the overall complexity of integrating multiple sensors while maintaining the reliability benefits of sensor fusion.
4Measurement precision
If head tracking system is used to determine user position, then measurement precision is improved, but loss of time increases due to processing latency
Solution Approach 1:
The patent performs preliminary action by having the inertial motion tracking system continuously track device motion and maintain an up-to-date estimate of head position based on device movement. This preliminary motion tracking occurs in parallel with the image processing, so when image-based head position data becomes available, it can be immediately fused with the current motion tracking state, minimizing the effective latency of the precision head tracking system.
Data Source
AI summary
A device configured with a user interface (UI) that changes based on a position of a user determines the position of the user through multiple data sources including camera based head tracking and output from motion sensors such as a gyroscope. Each data source may output its own estimated head position. The device may apply a reliability weight to the head position determined by each data source. A composite head position is then determined from the weighted position. The composite position is then used to render the UI.