Feature Quality Filtering for Efficient Environment Mapping
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Solution Overview
Problem
The density and size of localization data collected by mobile devices for SLAM and AR applications can slow down object identification and impact storage resources, as existing methods do not effectively manage data quality and redundancy.
Innovation Solution
Assigning quality values to features based on consistency, observations, and dynamic characteristics, and reducing localization data by removing low-quality features, non-visual sensor data, and geometrically compressing features, while maintaining high-quality mapping capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If localization data is collected densely for SLAM and AR applications, then mapping accuracy and object identification capability are improved, but processing speed decreases and storage resources are consumed
Solution Approach 1:
The patent applies local quality by assigning different quality values to different features based on their individual characteristics (e.g., stability, observability, geometric properties). High-quality features that contribute more to mapping accuracy are retained, while low-quality features are removed. This selective retention maintains mapping precision while reducing the overall data volume that requires processing.
Solution Approach 2:
The patent changes the parameter of feature quality by introducing a quality metric that evaluates each feature's contribution to SLAM performance. By filtering features based on this quality parameter, the system reduces data density while preserving the most informative features, thereby maintaining accuracy while improving processing speed.
2Measurement precision
If localization data is collected densely for SLAM and AR applications, then mapping accuracy and object identification capability are improved, but storage resources are consumed
Solution Approach 1:
The patent evaluates each feature's local quality based on characteristics such as stability, observability, and geometric properties. Features with high quality values are retained for storage, while low-quality features are discarded. This selective approach maintains the accuracy required for reliable mapping while significantly reducing the total quantity of stored localization data.
Solution Approach 2:
The patent discards low-quality features that contribute minimally to mapping accuracy, thereby reducing storage requirements. The system recovers and retains only the high-quality features that are essential for maintaining SLAM and AR functionality, achieving efficient storage utilization without sacrificing performance.
3Reliability
If all collected localization data is retained for processing, then comprehensive environmental mapping is achieved, but processing overhead and computational load increase
Solution Approach 1:
The patent assigns quality values to features based on their individual characteristics, identifying which features provide the most reliable information for environmental mapping. By processing only high-quality features, the system maintains mapping completeness while reducing the computational load and energy consumption associated with processing all collected data.
Solution Approach 2:
The patent applies partial action by selectively processing only the necessary subset of localization data (high-quality features) rather than all collected data. This approach achieves sufficient mapping reliability without the excessive computational overhead of processing every data point, optimizing the balance between completeness and resource usage.
Data Source
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AI summary
An electronic device [100] reduces localization data [230] based on feature characteristics identified from the data [225]. Based on the feature characteristics, a quality value can be assigned to each identified feature [343], indicating the likelihood that the data associated with the feature will be useful in mapping a local environment of the electronic device. The localization data is reduced by removing data associated with features have a low quality value, and the reduced localization data is used to map the local environment of the device [235] by locating features identified from the reduced localization data in a frame of reference for the electronic device.