LiDAR-Based POI Feature Extraction for Privacy-Safe Mobile Mapping
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Solution Overview
Problem
Current point-of-interest (POI) updating methods, such as user-provided details and social media sharing, often lack sufficient information and raise privacy concerns, posing challenges for service providers to develop effective POI feature extraction and updating technologies.
Innovation Solution
The use of Light Detection and Ranging (LiDAR) sensors on portable devices to capture scans of locations, determining the context of POIs, and selecting feature recognition parameters for efficient feature detection analysis, allowing for precise identification of POI features without exposing sensitive information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If social media is used for POI updates, then user sharing capability is improved, but privacy security deteriorates
Solution Approach 1:
The patent extracts only the necessary POI feature information from the environment using LiDAR scanning and image processing, separating useful data (POI features, dimensions, layout) from sensitive information (personal appearance, precise location coordinates). This allows user sharing capability while protecting privacy security by publishing only extracted features without exposing identifiable personal data.
Solution Approach 2:
The patent introduces LiDAR scanning technology and automated feature extraction algorithms as intermediaries between the user and the POI information. Instead of users directly sharing personal data or photos, the system uses these intermediary technologies to automatically capture and process environmental data, extracting POI features without requiring users to expose their personal information.
2Ease of manufacture
If traditional POI updating methods are used, then implementation simplicity is improved, but information completeness deteriorates
Solution Approach 1:
The patent enables the system to automatically perform POI feature extraction and updating without requiring manual input from users or POI owners. The LiDAR scanner automatically captures the environment, the image processing algorithms automatically extract features, and the system automatically updates the POI database. This self-service approach maintains implementation simplicity while dramatically improving information completeness by capturing detailed spatial and dimensional data.
Solution Approach 2:
The patent replaces manual POI updating methods (mechanical human input) with automated LiDAR scanning and computational image processing systems. Instead of relying on users to manually enter POI details, the system uses optical scanning and algorithmic feature extraction to automatically capture comprehensive POI information, including dimensions, layout, and spatial relationships.
3Measurement precision
If LiDAR scanning is used for POI feature extraction, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent leverages the LiDAR scanner already integrated into modern portable devices for its primary navigation and mapping functions. By repurposing this existing multi-functional component for POI feature extraction, the system achieves high measurement precision without significantly increasing device complexity. The same LiDAR hardware serves multiple purposes: navigation, mapping, and automated POI documentation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables more accurate and privacy-compliant POI feature extraction and updating, providing detailed information about locations while preserving user privacy, and is more efficient in low-light conditions and robust across multiple angles.
Implementation Method 1
receiving a Light Detection and Ranging (LiDAR) scan of a location captured using a LiDAR sensor of a portable device
Data Source
AI summary
An approach is provided for extracting point-of-interest features based on depth sensor data captured by mobile devices. The approach involves, for instance, receiving a Light Detection and Ranging (LiDAR) scan of a location captured using a LiDAR sensor of a portable device. The approach also involves determining a context of the location, a point of interest (POI) associated with the location, or a combination thereof. The approach further involves selecting a feature recognition parameter based on the context. The feature recognition parameter performs a feature detection analysis of a scenery depicted in the scan. The approach further involves initiating the feature detection analysis of the scan based on the feature recognition parameter to identify a feature, an attribute of the feature, or a combination thereof in the scenery.