Sensor Fusion Direction Determination for User Tracking
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Solution Overview
Problem
Existing devices face challenges in accurately determining the direction of a user within a physical space, especially in environments with obstacles and varying conditions, which affects their operation and user experience.
Innovation Solution
The system employs sensor fusion techniques using sound data from a microphone array and image data from cameras, combined with map data to accurately determine the user's direction, incorporating variance modification to improve accuracy and adaptability, allowing the device to move autonomously or adjust its orientation accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor fusion techniques are used to improve direction determination accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent combines data from multiple sensors (microphone array for sound direction, cameras for image data, and map data) to determine user direction. This sensor fusion approach merges multiple data sources to achieve higher measurement precision than any single sensor could provide alone, directly resolving the contradiction by accepting increased complexity as necessary for improved accuracy.
Solution Approach 2:
The system introduces map data as an intermediary element that helps resolve ambiguities in sensor data. By comparing sensor-derived directions with pre-stored map information about obstacles and open regions, the system can filter out incorrect direction estimates, thereby improving measurement precision while managing complexity through the use of auxiliary reference data.
2Adaptability or versatility
If multiple sensors and data sources are integrated to improve adaptability, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system achieves environmental adaptability by designing a multi-functional architecture that processes various data types (sound, image, map data) through a unified direction determination framework. This universal approach allows the same system to adapt to different environments and conditions without requiring separate specialized subsystems, improving adaptability while controlling complexity through functional integration.
Solution Approach 2:
The system dynamically adjusts its operation by modifying variance values based on environmental conditions and sensor data quality. This dynamic adaptation allows the system to weigh different sensor inputs appropriately for current conditions, enhancing versatility while managing complexity through adaptive parameter adjustment rather than structural changes.
3Measurement precision
If variance modification is applied to improve direction determination accuracy, then measurement precision is improved, but computational requirements increase
Solution Approach 1:
The system improves measurement precision by dynamically modifying variance parameters associated with different sensor inputs. By adjusting these statistical parameters based on environmental conditions and data quality, the system achieves more accurate direction determination without requiring fundamentally more complex computational algorithms, thereby managing power requirements while improving precision.
Data Source
AI summary
A device has a microphone array that acquires sound data and a camera that acquires image data. A portion of the device may be moveable by one or more actuators. Responsive to the user, the portion of the device is moved toward an estimated direction of the user. The estimated direction is based on sensor data including the sound data and the image data. First variance values for individual sound direction values are calculated. Data derived from the image data or data from other sensors may be used to modify the first variance values and determine second data comprising second variances. The second data may be processed to determine the estimated direction of the user. For example, the second data may be processed by both a forward and a backward Kalman filter, and the output combined to determine an estimated direction toward the user.