Visual Localization Map Generation Using 3D Model Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current visual localization methods require extensive map generation processes, which are time-consuming and costly, especially for outdoor environments, and struggle with precision due to the inclusion of hindering factors like trees and roads.
Innovation Solution
A method utilizing 3D model data based on aerial photos to generate a feature point map, allowing for visual localization by rendering images from a virtual camera pose and excluding unnecessary objects, enabling precise 3D position and pose estimation with reduced data noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional visual localization methods are used with extensive map generation processes, then comprehensive map coverage is achieved, but time consumption and cost increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-generating 3D model data from aerial photos and pre-extracting feature points before actual localization is needed. The 3D model data including depth information is prepared in advance, and feature points are extracted and stored in a feature point map beforehand, so that when localization is required, the system can quickly perform matching without time-consuming map generation processes.
2Measurement precision
If traditional map generation includes all objects in the scene, then complete environmental representation is achieved, but precision deteriorates due to hindering factors like trees and roads
Solution Approach 1:
The patent applies the extraction principle by selectively removing hindering objects from the 3D model data. Objects that obstruct visual localization such as trees, roads, and other irrelevant elements are identified and excluded from the feature point map. Only relevant and useful feature points are extracted and stored, creating a simplified yet effective map for accurate localization without the noise from hindering factors.
3Productivity
If 3D model data from aerial photos is used directly, then map generation efficiency is improved, but data quality deteriorates due to noise and irrelevant information
Solution Approach 1:
The patent converts the potential harm of noise and irrelevant information in aerial photo-based 3D model data into a benefit by systematically filtering and processing the data. The method transforms the raw, noisy 3D model data into a refined feature point map by extracting only meaningful feature points and removing irrelevant information, thereby turning the initial data quality issue into an opportunity for creating a high-quality, optimized localization map.
Data Source
AI summary
A method of generating a map for visual localization includes specifying a virtual camera pose by using 3-dimensional (3D) model data which is based on an image of an outdoor space captured from the air; rendering the image of the outdoor space from a perspective of the virtual camera, by using the virtual camera pose and the 3D model data; and generating a feature point map by using the rendered image and the virtual camera pose.