Vision-Based Localization Using Semantic Search Space Pruning
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Solution Overview
Problem
Traditional methods for road geometry modeling and vision-based mapping are resource-intensive and time-consuming, making them inefficient for accurate localization of vehicles, especially in environments without satellite navigation or with low accuracy, which is problematic for autonomous vehicle control.
Innovation Solution
A method using sensor data from vehicles to identify and classify environmental features, compare them with map image data, and register a localized location based on semantic classifications, reducing the search space and processing capacity to achieve accurate and efficient localization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional vision-based mapping methods are used to achieve accurate localization, then measurement precision is improved, but productivity deteriorates due to resource-intensive processing and time-consuming operations
Solution Approach 1:
The patent segments the feature comparison process by classifying features into semantic categories (e.g., lane markings, traffic signs, pedestrians) and only comparing features of the same category. This segmentation reduces the search space from all possible feature pairs to only relevant category-matched pairs, significantly improving processing efficiency while maintaining localization accuracy
Solution Approach 2:
The patent applies local quality by treating different semantic categories of features differently in the comparison process. Instead of uniform comparison of all features, the system selectively compares only relevant feature categories (e.g., comparing lane markings with lane markings, traffic signs with traffic signs), optimizing the comparison process for each local feature type
2Measurement precision
If comprehensive feature comparison is performed to ensure accurate localization, then measurement precision is improved, but loss of time increases due to extensive search space exploration
Solution Approach 1:
The patent performs preliminary classification of features into semantic categories before the comparison stage. By pre-organizing features by category, the system eliminates the need for exhaustive search during localization, reducing time loss while ensuring accurate matching of corresponding features between map data and sensor data
Solution Approach 2:
The patent extracts and removes irrelevant features from the comparison process by using semantic classification. Features that do not match in category are excluded from comparison, effectively taking out unnecessary computations and reducing the time required for localization while maintaining precision through focused comparison of relevant features
Data Source
AI summary
Methods described herein relate to reducing the computational intensity of vision-based localization. Methods may include: receiving sensor data from a vehicle traveling along a road; identifying one or more features of the environment from the sensor data; classifying the one or more identified features into one or more of a plurality of semantic classifications for the features; identifying map image data based on an identified location of the vehicle; identifying one or more features in the map image data; comparing one or more identified features of a first semantic classification with one or more features of the map image data of the first semantic classification; and registering a localized location of the vehicle within the environment based, at least in part, on the one or more identified features of the first semantic classification corresponding to the one or more features of the map image data of the first semantic classification.