Mobile Position Estimation via Motion and Light Sensor Fusion
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Solution Overview
Problem
Existing mobile devices face challenges in accurately determining the position of a device using motion and light sensors due to disagreements in position estimates and the unreliability of light sensor data, which is heavily quantized and influenced by ambient light conditions.
Innovation Solution
A method that generates a position estimate for a mobile device by analyzing data from motion sensors and light sensors, such as accelerometers, magnetometers, ambient light sensors, proximity sensors, and cameras, by producing a list of candidate positions and eliminating less likely options based on confidence metrics and hard-coded rules to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If light sensors (ALS, proximity sensor, camera) are used to determine device position, then position information can be obtained, but the position estimates are unreliable due to heavy quantization and dependence on ambient light conditions
Solution Approach 1:
The patent combines motion sensor data (accelerometer, magnetometer, gyroscope) with light sensor data (ambient light sensor, proximity sensor, camera) to determine device position. The motion sensor provides reliable position information independent of ambient light, while the light sensor provides complementary information about lighting conditions. By merging both sensor types, the system achieves both reliability and precision in position estimation.
Solution Approach 2:
The patent uses an intermediary processing layer that receives quantized light sensor output and motion sensor output, then produces refined position estimates. This intermediary layer filters out the unreliability of quantized light sensor data by cross-referencing with motion sensor data, effectively mediating between the two sensor types to produce accurate position information.
2Reliability
If motion sensors (accelerometer, magnetometer, gyroscope) are used to determine device position, then reliable position information can be obtained, but the system complexity increases due to processing multiple sensor types
Solution Approach 1:
The patent segments the position estimation process into distinct modules: motion sensor processing, light sensor processing, and fusion processing. Each sensor type is processed separately through dedicated algorithms, then the results are combined in a final fusion step. This segmentation reduces overall system complexity by making each component independent and easier to optimize.
Solution Approach 2:
The patent implements a universal position estimation framework that can handle multiple sensor types through a common processing architecture. The same basic framework processes motion sensors, light sensors, and their combinations, making the system multi-functional and reducing complexity through code reuse and standardized processing pipelines.
3Productivity
If light sensor output is used directly for position determination, then processing is simple, but the quantization causes loss of information and reduces measurement precision
Solution Approach 1:
The patent applies preliminary processing to light sensor output by using the quantized values as initial estimates and then refining them through fusion with motion sensor data. The quantized light sensor output is processed first to provide a quick initial position estimate, then subsequently refined in the fusion step to achieve high precision, maintaining processing efficiency while improving accuracy.
Data Source
AI summary
A position estimate for a mobile device is generated using data from motion sensors, such as accelerometers, magnetometers, and/or gyroscopes, and data from light sensors, such as an ambient light sensor, proximity sensor and/or camera intensity sensor. A plurality of proposed positions with associated likelihoods is generated by analyzing information from the motion sensors and a list of candidate positions is produced based on information from the light sensors. At least one of the plurality of proposed positions is eliminated using the list of candidate positions and a position estimate for the mobile device is determined based on the remaining proposed positions and associated likelihoods. The proposed positions may be generated by extracting features from the information from the motion sensors and using models to generate likelihoods for the proposed positions. The likelihoods may be filtered over time. Additionally, a confidence metric may be generated for the estimated position.