Map Data Update via Static Object Extraction from Multi-Vehicle Images
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Solution Overview
Problem
Conventional autonomous vehicle systems using cameras and radar face limitations in accurately reflecting changes in road environments, such as new installations or lane changes, due to difficulties in quickly updating map data with information from moving objects or dynamic conditions.
Innovation Solution
An image processing method that receives images from multiple vehicles, performs image registration using feature points, applies a transparency process, and updates map data by comparing static objects in the images with existing map data, allowing for real-time reflection of changes in the road environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If map data is updated using information from moving objects and dynamic conditions, then the accuracy and timeliness of map data is improved, but the difficulty of detecting and measuring static objects among dynamic elements increases
Solution Approach 1:
The system performs preliminary classification of objects as static or dynamic before updating map data. By pre-identifying static objects (roads, buildings, signs) versus dynamic objects (vehicles, pedestrians) in the plurality of images, the system prepares structured data that facilitates accurate and timely map updates without being overwhelmed by the complexity of simultaneous motion detection
Solution Approach 2:
The system extracts only the necessary static object information from the plurality of images for map data updates, separating relevant static features from irrelevant dynamic elements. This extraction process focuses computational resources on identifying and updating permanent road features while filtering out transient moving objects, thereby improving measurement precision without proportionally increasing detection difficulty
2Loss of information
If multiple images from multiple vehicles are processed to update map data, then the completeness and accuracy of map information is improved, but the processing time and computational complexity increases
Solution Approach 1:
The system merges images from multiple vehicles captured at different positions and angles to create a comprehensive view of the road environment. By combining multiple image sources and performing unified static object detection across all images, the system achieves complete map information coverage while avoiding redundant processing of identical features that would occur if each image were processed independently
Solution Approach 2:
The system performs preliminary organization and alignment of multiple images before detailed processing, establishing a coordinated framework that identifies which static objects appear in which images. This preliminary structuring enables subsequent processing to focus only on unique or newly detected objects rather than re-analyzing the entire image set, thereby reducing overall processing time while maintaining information completeness
Data Source
AI summary
Provided is an image processing method. The image processing method includes receiving images acquired from a plurality of vehicles positioned on a road; storing the received images according to acquisition information of the received images; determining a reference image and a target image based on images having the same acquisition information among the stored images; performing an image registration using a plurality of feature points extracted from each of the determined reference image and target image; performing a transparency process for each of the reference image and the target image which are image-registered; extracting static objects from the transparency-processed image; and comparing the extracted static objects with objects on map data which is previously stored and updating the map data when the objects on the map data which is previously stored and the extracted static objects are different from each other.