Neural Network Localization Using Image and Map Vector Comparison
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Solution Overview
Problem
The challenge of accurately determining the precise location of a device, especially in urban environments like urban canyons, where traditional methods such as GPS and Wi-Fi signals may fail to provide reliable and precise location data.
Innovation Solution
A method that utilizes a neural network to determine image-based vectors from a device to features in an image, which are then compared to map-based vectors to refine the device's location. This approach requires only a limited amount of map data and can determine a refined location efficiently, even in challenging environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional GPS and Wi-Fi signal methods are used for device localization, then the system is simple and easy to operate, but the measurement precision and reliability of location data deteriorate in urban environments
Solution Approach 1:
The patent combines multiple localization approaches by integrating image-based vector comparison with traditional GPS and Wi-Fi signal methods. The system merges coarse location data from GPS/Wi-Fi with refined location data derived from comparing image-based vectors (obtained via neural network) with map-based vectors, creating a hybrid system that achieves high precision in urban environments while maintaining operational simplicity through automated processing.
Solution Approach 2:
The patent introduces an intermediary processing layer that compares image-based vectors with map-based vectors to refine location data. This intermediary step acts as a mediator between the coarse GPS/Wi-Fi location and the final precise location, enabling accurate localization in urban canyons without requiring complete replacement of traditional systems.
2Measurement precision
If more map data is used to improve location accuracy, then the measurement precision improves, but the loss of time and computational resources increases
Solution Approach 1:
The patent extracts only the essential map data features needed for vector comparison rather than processing complete map datasets. By focusing on specific geometric features and vectors relevant to location refinement, the system achieves high precision while minimizing data processing time and computational resource requirements.
Solution Approach 2:
The patent applies partial action by using a limited, targeted subset of map data specifically for vector comparison purposes. Rather than processing all available map information, the system selectively uses only the portions necessary for refining location accuracy, thereby reducing processing time while maintaining precision.
Data Source
AI summary
A method, apparatus and computer program product are provided for improving localization of a device. In this regard, a coarse location of the device is determined and a neural network is utilized to determine one or more image-based vectors from the device to respective features in an image captured by an image capture device associated with the device. At one or more location points defined relative to the coarse location of the device, (a) one or more map-based vectors extending from a respective location point to respective features as defined by map data are compared to (b) the one or more image-based vectors. Based on the comparison, a refined location of the device is determined. A method, apparatus and computer program are also provided for training the neural network to determine an image-based vector from the device to a feature in an image captured by the device.