Position Estimation System Using Image Feature Matching
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Solution Overview
Problem
Existing position estimation systems face inefficiencies in identifying the position of moving bodies, such as vehicles, due to long processing times when creating travel route data from images captured by vehicle-installed cameras, and inaccuracies in position information acquired from images.
Innovation Solution
A position estimation system comprising an acquisition circuit, memory, and processor that acquires and processes first position information and images, utilizing a feature database and learning model to estimate positions efficiently by matching image features with stored map data, thereby reducing processing time and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If travel route data is created from images captured by vehicle-installed cameras to identify position, then position identification accuracy is improved, but processing time becomes excessively long
Solution Approach 1:
The system pre-generates travel route data from map information before actual position identification is needed. This preliminary creation of reference data allows the system to quickly match captured images against preprocessed route data, significantly reducing processing time during actual position identification while maintaining accuracy
Solution Approach 2:
The system divides the image processing task into segments by comparing only specific regions of interest between captured images and pre-generated travel route data. This segmentation approach reduces the overall processing load while maintaining position identification accuracy by focusing computational resources on key discriminative features
2Adaptability or versatility
If position information is acquired from images to estimate moving body position, then position estimation capability is improved, but accuracy deteriorates due to large errors in acquired position information
Solution Approach 1:
The system uses feedback from image matching results to correct and refine acquired position information. By comparing captured images with pre-generated travel route data and iteratively adjusting position estimates, the system compensates for large errors in initial position information from GPS or other sensors, significantly improving position estimation accuracy
Solution Approach 2:
The system replaces reliance on mechanical positioning systems (GPS, inertial sensors) with an optical-based position estimation method using image recognition and comparison. This substitution allows the system to overcome limitations of mechanical positioning systems, particularly in environments where GPS signals are weak or inaccurate, by using visual features from captured images to determine position
Data Source
AI summary
An acquisition circuit acquires first position information indicating a position and a first image captured by a camera at the position indicated by the first position information. A memory stores second position information indicating a prescribed position on a map and feature information extracted from a second image corresponding to the prescribed position. The second position information is associated with the feature information. A processor estimates the position indicated by the first position information on the basis of the second position information in the case that the position indicated by the first position information falls within a prescribed range from the prescribed position indicated by the second position information and the first image corresponds to the feature information.