Spatial Image Indexing Using SLAM for Indoor Video Mapping
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Solution Overview
Problem
Conventional methods require manual annotation of image locations within indoor spaces, which is inefficient and time-consuming, especially when capturing multiple images or integrating them with video in environments where GPS or RF signals are unreliable.
Innovation Solution
A spatial indexing system that uses a SLAM algorithm to automatically determine the spatial locations of video frames and images without manual annotation, generating an immersive model for efficient navigation and integration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation of image locations is used, then location accuracy can be ensured, but time consumption and labor efficiency deteriorate significantly
Solution Approach 1:
The patent replaces the manual mechanical annotation process with an automated computer vision system. The system uses image processing algorithms to automatically detect and recognize location information from images, substituting human manual labeling with automated computational methods. This resolves the contradiction by maintaining location accuracy through algorithmic detection while eliminating time-consuming manual annotation.
Solution Approach 2:
The system enables images to self-annotate their location information automatically. By extracting location data directly from the image content through computer vision techniques, the images themselves provide the annotation information without requiring external manual intervention. This self-service approach maintains precision while dramatically reducing time consumption.
2Extent of automation
If GPS or RF signals are used for location tagging, then automated location identification is achieved, but reliability deteriorates in indoor environments
Solution Approach 1:
The patent introduces image content as an intermediary to bridge the gap between automated location identification and reliability. Instead of relying directly on GPS/RF signals that fail indoors, the system uses visual features within images as a mediator to infer and determine location. This intermediary approach maintains automated identification while achieving reliability in indoor environments where traditional signals fail.
Solution Approach 2:
The system substitutes GPS/RF signal-based location determination with image-based visual recognition. By replacing the unreliable electromagnetic signal mechanism with a visual feature recognition mechanism, the system achieves both automated location identification and reliability in indoor settings where GPS/RF signals are unavailable or unreliable.
3Adaptability or versatility
If multiple images are captured to monitor space changes, then monitoring coverage is improved, but the complexity of integrating and managing images increases
Solution Approach 1:
The patent replaces manual integration processes with automated image processing and matching algorithms. The system automatically compares multiple images, identifies changes, and integrates them into a coherent monitoring dataset without requiring manual intervention. This substitution maintains comprehensive monitoring coverage while reducing integration complexity through automation.
Solution Approach 2:
The system enables images to self-integrate with other images through automated feature matching and change detection algorithms. Each image automatically finds its relationship to other images in the sequence, identifying temporal and spatial connections without external management. This self-service integration maintains comprehensive monitoring coverage while eliminating complex manual management procedures.
Data Source
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AI summary
A spatial indexing system receives a video that is a sequence of frames depicting an environment, such as a floor of a construction site, and performs a spatial indexing process to automatically identify the spatial locations at which each of the images were captured. The spatial indexing system also generates an immersive model of the environment and provides a visualization interface that allows a user to view each of the images at its corresponding location within the model.