Camera-Assisted Indoor Navigation Without GPS Hardware
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Solution Overview
Problem
Existing navigation systems in facilities, such as airports and hospitals, rely on GPS and additional hardware like Bluetooth, Infrared, and Wi-Fi, which are impractical due to hardware requirements and GPS inaccessibility indoors, making it difficult for users to navigate effectively.
Innovation Solution
A camera-assisted navigation system using machine learning techniques for object detection and classification, generating a virtual path based on facility model data without additional hardware, utilizing object detection models, classification models, and path classification models to provide navigation information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS and additional hardware (Bluetooth, Wi-Fi, RFID) are used for indoor navigation, then location information accuracy is improved, but device complexity and hardware requirements increase
Solution Approach 1:
The patent extracts the navigation function from complex hardware systems (GPS, Bluetooth, Wi-Fi, RFID) and implements it using only the camera and machine learning algorithms. This removes the need for additional hardware while maintaining navigation capability through image-based location estimation.
Solution Approach 2:
The patent replaces the mechanical/electromagnetic hardware systems (GPS satellites, Bluetooth transmitters, Wi-Fi access points, RFID tags) with an optical system (camera) and computational algorithms (machine learning). This substitution eliminates hardware complexity while achieving the same navigation function.
2Reliability
If GPS services are used for navigation, then location information is provided, but accessibility in indoor facilities is worsened due to satellite line-of-sight requirements
Solution Approach 1:
Instead of relying on external satellite signals that cannot penetrate buildings, the patent inverts the approach by using the camera to capture images of the indoor environment itself. The system then uses machine learning to interpret these images and determine location, making navigation work inside facilities rather than requiring external signals.
3Measurement precision
If additional hardware is deployed for indoor navigation, then navigation accuracy is improved, but ease of operation and user accessibility are worsened
Solution Approach 1:
The patent makes the navigation system self-sufficient by using the device's existing camera to capture and process environmental images. The machine learning model automatically extracts location information from these images without requiring users to set up additional hardware or configure complex settings, making the system easy to operate while maintaining high accuracy.
Data Source
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AI summary
The subject disclosure provides a computer-implemented method and computer system for use in navigating in a facility. For example, the computer-implemented method comprises: receiving, from a camera, at least one image; estimating, by a processor, a current location of the camera in the facility based on the at least one image and model data of the facility; generating, by the processor, a virtual path from the current location of the camera to a destination location in the facility using the model data of the facility; and generating and outputting navigation information to the destination location according to the virtual path.