Machine-Learning Camera Orientation from Imagery in Urban Environments
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Solution Overview
Problem
Accurately determining the geographic orientation of mobile devices remains challenging due to interference from surrounding structures and imprecision in magnetometer readings, particularly in urban environments.
Innovation Solution
A method and system that utilize a machine-learning model to determine geographic orientation based on imagery captured by a camera, considering factors like illumination variance, building positions, and text recognition, to accurately orient the device relative to a travelway.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS receivers are used to determine geographic orientation, then location data can be obtained, but accuracy deteriorates when the device is stationary or subject to interference from surrounding structures
Solution Approach 1:
The patent introduces imagery data as an intermediary element between the device and the environment. By capturing images of surrounding structures and using machine learning models to extract orientation information from these images, the system mediates the measurement process to avoid direct interference from the same surrounding structures that affect GPS signals.
Solution Approach 2:
The patent replaces the mechanical/GPS-based orientation determination system with an optical/image-based system. Instead of relying on satellite signals that are blocked by urban structures, the system uses camera imagery and machine learning algorithms to determine orientation, substituting a different physical domain (optics and computation) for the failing mechanical system.
2Measurement precision
If magnetometers are used to determine geographic orientation, then orientation data can be obtained, but precision deteriorates because the devices themselves interfere with magnetometers
Solution Approach 1:
The patent extracts the orientation determination function from the device's internal sensors (magnetometers) and relocates it to external imagery analysis. By taking the orientation measurement capability out of the device body and placing it in the captured images processed by remote or local machine learning models, the system eliminates self-interference entirely.
Solution Approach 2:
The patent introduces imagery as an intermediary medium that carries orientation information without being affected by device-generated magnetic interference. The machine learning model acts as another intermediary layer that processes the imagery to extract orientation data, creating a measurement chain that is completely isolated from electromagnetic interference sources within the device.
3Measurement precision
If machine-learning models are used to determine geographic orientation based on imagery, then orientation accuracy improves, but computational complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on large datasets of imagery with known orientations before deployment. This pre-computation phase creates ready-to-use models that can be deployed on mobile devices, transferring the heavy computational burden from runtime operation to an offline training phase, thereby reducing real-time complexity while maintaining high accuracy.
Data Source
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AI summary
The present disclosure is directed to determining geographic orientation based at least in part on imagery. In particular, the methods and systems of the present disclosure can: receive data generated by a camera (118) and representing imagery that includes at least a portion of a physical real-world environment comprising the camera (118) and a travelway (312); and determine, based at least in part on the data and a machine-learning model, a geographic orientation of the camera (118) with respect to the travelway (312).