AR Point Cloud Filtering for Low-Accuracy GNSS Zones
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Solution Overview
Problem
Linking virtual reality (VR) and augmented reality (AR) devices to high-accuracy global navigation satellite system (GNSS) data has proven difficult, hindering the integration of accurate spatial data in AR environments.
Innovation Solution
A method and system for capturing point clouds using an AR device that determines GNSS points, projects depth images into 3D space, filters points based on accuracy, and displays only high-accuracy points within a defined zone, correlating the AR reference frame with a geospatial reference frame for precise spatial data integration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If depth images are projected into 3D space to create point clouds, then spatial data coverage is improved, but data accuracy deteriorates due to low-accuracy GNSS points
Solution Approach 1:
The patent applies local quality by creating zones of different accuracy levels around high-accuracy GNSS points. Points within these zones are accepted with their original accuracy, while points outside are filtered or rejected. This allows the system to maximize spatial coverage in areas with reliable GNSS data while maintaining strict accuracy standards only where needed, resolving the contradiction between coverage quantity and data accuracy.
Solution Approach 2:
The patent segments the spatial data into multiple point clouds based on GNSS accuracy levels. High-accuracy points form one dataset, low-accuracy points form another, and they are processed differently. This segmentation allows the system to handle coverage and accuracy requirements separately for different data subsets, enabling both broad coverage and high precision where required.
2Quantity of substance
If all captured points are displayed, then data completeness is improved, but data reliability deteriorates due to inclusion of low-accuracy points
Solution Approach 1:
The patent implements local quality by establishing geographic zones around high-accuracy GNSS points where data acceptance criteria differ from areas outside these zones. Within zones, points are accepted with higher reliability standards, while outside zones, points are either filtered out or marked with lower reliability indicators. This resolves the contradiction by ensuring data reliability is maintained in critical areas while preserving overall data completeness.
Solution Approach 2:
The patent introduces zones as an intermediary mechanism between raw point cloud data and the final displayed output. These zones act as a filtering layer that mediates which points are displayed based on their spatial relationship to high-accuracy reference points. This intermediary structure allows the system to balance completeness and reliability by selectively displaying points based on zone membership rather than applying a uniform filter to all data.
3Measurement precision
If point clouds are filtered to remove low-accuracy points, then data accuracy is improved, but data coverage is reduced
Solution Approach 1:
The patent applies local quality by creating spatial zones where different filtering rules apply. Within zones surrounding high-accuracy GNSS points, strict filtering removes only points that don't meet high accuracy standards. Outside these zones, the filtering is more lenient or points are accepted with lower accuracy thresholds. This resolves the contradiction by maintaining high data accuracy in zone areas while preserving broader data coverage in non-zone areas.
Solution Approach 2:
The patent segments the point cloud processing into multiple passes or categories based on spatial location relative to high-accuracy reference points. Points are divided into those within zones and those outside zones, with different filtering criteria applied to each segment. This segmentation allows the system to achieve high accuracy for critical points while maintaining overall data coverage through lenient handling of peripheral points.
4Measurement precision
If AR reference frame is correlated with geospatial reference frame, then spatial integration accuracy is improved, but system complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-establishing zones around high-accuracy GNSS points before point cloud processing and correlation operations. These zones are created in advance based on the spatial distribution of high-accuracy reference points. By having these zones pre-defined, the system simplifies the correlation process between AR and geospatial reference frames, as points can be quickly classified based on their zone membership rather than performing complex real-time calculations, thus reducing system complexity while maintaining high spatial integration accuracy.
Data Source
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AI summary
Techniques for capturing and displaying point clouds using an AR device are described. A GNSS point of the AR device is determined based on received satellite signals. A depth image is captured while the AR device is positioned at the GNSS point. The depth image is projected into 3D space to obtain a point cloud having a set of points. An accuracy of the GNSS point is determined. In response to determining that the accuracy of the GNSS point is below a threshold, the GNSS point is determined to be a low-accuracy GNSS point, points from the set of points that are outside of a zone surrounding a previously determined high-accuracy GNSS point are removed, and remaining points from the set of points that are inside the zone are displayed on a display of the AR device.