Vehicle Localization Search Space Pruning with Semantic Map Features
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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 in environments without satellite navigation or with low satellite-based navigation accuracy, which is problematic for autonomous vehicle control.
Innovation Solution
A method that uses 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 localization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional vision-based mapping methods are used to identify location through environment recognition, then localization accuracy is improved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent segments the feature comparison process by dividing features into different semantic classifications (e.g., road geometry features, traffic sign features, landmark features). The system compares only features of the same semantic classification between sensor data and map data, rather than performing exhaustive comparisons across all features. This segmentation reduces the search space and computational complexity while maintaining localization accuracy.
Solution Approach 2:
The patent applies local quality by making different parts of the feature set have different comparison priorities. Semantically classified features are assigned specific comparison rules and weights based on their classification type. For example, road geometry features may be compared using specific geometric transformations, while traffic sign features use template matching. This localized optimization of comparison strategies improves efficiency without sacrificing overall accuracy.
2Measurement precision
If comprehensive feature comparison is performed to establish accurate location, then localization accuracy is improved, but computational complexity and processing requirements increase
Solution Approach 1:
The patent segments the feature comparison process by dividing features into different semantic classifications (e.g., road geometry features, traffic sign features, landmark features). The system compares only features of the same semantic classification between sensor data and map data, rather than performing exhaustive comparisons across all features. This segmentation reduces the search space and computational complexity while maintaining localization accuracy.
Solution Approach 2:
The patent performs preliminary actions by pre-classifying features into semantic categories before the actual localization process. Map features are pre-organized by semantic classification, and sensor data features are classified in real-time. This preliminary organization enables the system to quickly identify which feature subsets need comparison, reducing computational complexity during the critical localization phase.
3Manufacturing precision
If traditional road geometry modeling methods are used for map creation, then map accuracy is improved, but time consumption and cost increase significantly
Solution Approach 1:
The patent uses copying by creating semantic classifications from sensor data that mirror the structure of pre-existing map data. Instead of performing exhaustive measurements and calculations to create maps from scratch, the system copies the semantic organization and feature classifications from the map database and applies them to real-time sensor data. This enables rapid localization by comparing features within the same semantic categories, achieving high map accuracy without traditional time-consuming measurement processes.
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.