Camera-Based Vehicle Position Estimation in GPS-Denied Urban Areas
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Solution Overview
Problem
Existing vehicle position estimation methods, particularly in environments with high-rise buildings or complex city layouts, face challenges in accurately determining vehicle positions due to the limitations of Global Positioning System (GPS) signals.
Innovation Solution
A vehicle position estimation method that utilizes a camera to match landmarks from a high definition map with images captured by the camera, allowing for the estimation of vehicle position even in areas where GPS signals are unreliable.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If GPS-based position estimation is used, then the system is simple and cost-effective, but the measurement precision deteriorates in environments with high-rise buildings or complex city layouts
Solution Approach 1:
The patent combines GPS-based position estimation with landmark-based position estimation to create a hybrid system. The GPS provides initial position information, while landmark matching refines the position accuracy in GPS-denied environments. This merging allows the system to maintain simplicity while improving measurement precision in complex urban environments.
Solution Approach 2:
The patent introduces landmark-based position estimation as an intermediary method to bridge the gap between GPS availability and actual position accuracy. When GPS signals are blocked by high-rise buildings or complex city layouts, the landmark matching process serves as a mediator to provide accurate position estimation without requiring complex expensive equipment like LiDAR.
2Measurement precision
If expensive devices like LiDAR are used to improve position estimation accuracy, then the measurement precision improves, but the manufacturing cost increases
Solution Approach 1:
The patent replaces expensive long-lasting devices like LiDAR with cheaper alternatives. Instead of using costly active sensing equipment, the system uses standard cameras combined with pre-existing high-definition map data. This substitution dramatically reduces manufacturing costs while maintaining position estimation accuracy in complex urban environments.
Solution Approach 2:
The patent uses two-dimensional image data from cameras and corresponding two-dimensional landmark data from high-definition maps to estimate vehicle position. This copying approach using planar representations avoids the need for expensive three-dimensional sensing equipment like LiDAR, achieving cost-effective position estimation with sufficient accuracy.
3Measurement precision
If landmark-based position estimation is used, then the measurement precision improves in GPS-denied environments, but the device complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-storing high-definition map data with landmark information before the vehicle reaches GPS-denied environments. The system pre-processes and stores two-dimensional landmark data from aerial images, so when GPS signals are blocked, the vehicle can immediately perform landmark matching without requiring complex real-time processing or additional hardware.
Solution Approach 2:
The patent extracts only the essential two-dimensional landmark features from complex three-dimensional environments and stores them in high-definition maps. By extracting and storing only the necessary positional and visual information of landmarks beforehand, the system reduces the complexity of real-time processing while maintaining position estimation accuracy when needed.
Data Source
AI summary
In accordance with an aspect of the present disclosure, there is provided a vehicle position estimation method. The method comprises, obtaining a landmark-based initial position information of a camera by matching a landmark in a high definition map corresponding to a GPS-based initial position information of a vehicle to an image obtained by the camera of the vehicle, and obtaining estimated position information of the vehicle by comparing the image with a landmark in the high definition map corresponding to each of a plurality of candidate position information sampled based on the landmark-based initial position information of the camera.