Mobile Device Visual Localization Using SLAM-CNN Key Frames
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Solution Overview
Problem
Existing location determination methods for mobile devices, such as those using triangulation techniques and wireless signals, face limitations in accuracy and are affected by signal blockages, while object recognition approaches require significant resources and databases.
Innovation Solution
The use of a combination of simultaneous localization and mapping (SLAM) and convolution neural network (CNN) algorithms to generate and match key frames from imagery captured by a mobile device, correlating them to physical locations within a defined area, enhancing location determination accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If triangulation techniques and wireless signals are used for location determination, then the method is widely applicable, but the accuracy is limited and signal blockages occur
Solution Approach 1:
The patent introduces visual features from captured images as an intermediary element between the mobile device and its location. Instead of directly using wireless signals for location determination, the system captures images, extracts visual features (edges, corners, shapes), and matches these features against a database to determine location. This intermediary approach bypasses signal blockage issues by using visual information that can penetrate or work around obstacles.
Solution Approach 2:
The patent replaces the wireless signal-based location determination system with a vision-based system. Instead of relying on electromagnetic signals (triangulation, Wi-Fi, cellular), the system uses image capture and visual feature analysis. This substitution eliminates the fundamental limitation of signal blockages by using optical information processing rather than electromagnetic signal processing.
2Measurement precision
If object recognition approaches are used to improve location determination accuracy, then the accuracy improves, but large databases and considerable processing resources are required
Solution Approach 1:
The patent segments the complex object recognition process into distinct, manageable stages: image capture, edge detection, corner detection, shape recognition, and database matching. Each stage processes only specific visual features rather than attempting to recognize complete objects. This segmentation reduces the computational burden at each step while maintaining overall accuracy.
Solution Approach 2:
The patent extracts only the essential visual features (edges, corners, geometric shapes) from captured images, discarding redundant information such as color, texture, and full object context. By taking out only the critical geometric features needed for location determination, the system reduces database requirements and processing complexity while preserving location accuracy.
Data Source
AI summary
Various embodiments are directed to techniques for determining a current location of a mobile device. An apparatus includes a SLAM candidate component to identify a first candidate key frame matching a current captured frame by a first degree from an interval-based key frame set with key frames selected on a recurring interval from multiple earlier captured frames captured by mobile device camera of surroundings within a defined area, a CNN candidate component to identify a second candidate key frame matching the current captured frame.