Mobile Device Localization via Vehicle Image Analysis
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Solution Overview
Problem
Existing device localization techniques, such as GNSS and visual-based methods, face challenges in urban environments due to signal obstructions and scalability issues, leading to inaccurate and unreliable location data.
Innovation Solution
A vehicle locations database is established to enable improved localization by leveraging known vehicle locations, which are accurate and reliable, using radio-visual and image-based approaches that utilize vehicle information and computer vision techniques to determine a mobile device's location and orientation relative to nearby vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GNSS-based localization is used, then real-time location data can be obtained, but accuracy deteriorates in urban environments due to signal obstructions and multipath errors
Solution Approach 1:
The patent uses vehicles as intermediary objects for localization. Instead of directly using GNSS signals which are blocked in urban environments, the system captures images of vehicles whose positions are known through GNSS, and uses these vehicle images as intermediaries to infer the device's location. This intermediary approach bypasses the direct signal obstruction problem.
Solution Approach 2:
The patent replaces the mechanical/electromagnetic GNSS signal-based localization system with an image-based visual localization system. By substituting radio frequency signal processing with optical image capture and processing, the system achieves reliable localization in urban environments where GNSS signals are obstructed.
2Measurement precision
If visual-based localization methods are used, then high accuracy can be achieved, but scalability deteriorates due to the need for large databases of geo-tagged visual features
Solution Approach 1:
The patent extracts only the essential visual feature - vehicles - from the complex environment for localization purposes. Instead of requiring comprehensive databases of all geo-tagged visual features in an area, the system focuses specifically on detecting and using vehicles as localization anchors, significantly reducing database requirements while maintaining accuracy.
Solution Approach 2:
Vehicles serve multiple functions in this system: they are universal localization anchors that can be found throughout urban environments, provide known position references through their GNSS data, and serve as recognizable visual features for image-based localization. This multi-functionality eliminates the need for specialized databases for different location types.
3Adaptability or versatility
If sensor fusion algorithms relying on IMUs are used, then device movement information can be incorporated, but accuracy deteriorates over time due to sensor drift
Solution Approach 1:
The system uses periodic feedback from vehicle detections to correct and reset the accumulated drift from IMU sensors. Each time a vehicle is detected and its position used for localization, it provides a feedback reference point that recalibrates the device's position estimate, preventing long-term drift accumulation while maintaining continuous movement tracking capability.
Data Source
AI summary
Disclosed is an image-based approach for device localization. In particular, a mobile device may capture image(s) of a vehicle that is substantially proximate to the mobile device. Based on the image(s), the mobile device may (i) determine parameter(s) associated with the vehicle, and (ii) determine or obtain a location of the vehicle in accordance with the determined parameter(s). Additionally, the mobile device may use the image(s) as basis for determining a relative location indicating where the mobile device is located relative to the vehicle. Based on the location of the vehicle and on the relative location of the mobile device, the mobile device may then determine a location of the mobile device.