Vehicle Localization Maps Using Simplified Object Structures
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Solution Overview
Problem
Conventional SLAM-based methods for creating vehicle localization maps face inefficiencies due to reliance on punctiform representations of objects, which fail to accurately describe complex structures and result in reduced localization quality and increased data volume.
Innovation Solution
The method involves ascertaining data of objects in a vehicle's surroundings, identifying characteristic structures, combining them into simplification structures, and incorporating these structures into a feature-based localization map, allowing for semi-semantic analysis and improved localization quality by representing complex structures with multiple points rather than punctiform objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If punctiform representations of objects are used in SLAM-based localization maps, then the data volume is reduced and processing is simplified, but the localization quality and accuracy are reduced due to inability to accurately describe complex structures
Solution Approach 1:
The patent segments complex objects into multiple characteristic structures (e.g., corners, edges, surfaces) that can be individually identified and processed. Each characteristic structure is represented by multiple points rather than a single punctiform representation, enabling accurate description of complex geometries while maintaining manageable data through structured organization of these segmented elements
Solution Approach 2:
The patent transitions from punctiform (0-dimensional) representations to multi-point spatial distributions that capture the dimensional characteristics of objects. By representing objects with multiple points in 3D space that define characteristic structures, the system adds dimensional information without creating continuous volumetric data, thus improving localization accuracy while controlling data volume
2Measurement precision
If detailed characteristic structures of objects are identified and combined into simplification structures, then the localization accuracy is improved, but the processing complexity and computational requirements increase
Solution Approach 1:
The patent extracts only the essential characteristic structures (corners, edges, surfaces) from complete object models, taking out only the features necessary for localization. This extraction approach maintains high localization accuracy by preserving geometrically significant points while eliminating redundant data, thus reducing processing complexity compared to using full detailed models
Solution Approach 2:
The patent applies different levels of structural detail to different parts of objects based on their importance for localization. Characteristic structures that are critical for position determination (e.g., distinctive corners or edges) are represented with higher precision and more points, while less critical areas use simpler representations, optimizing the balance between accuracy and processing complexity
Data Source
AI summary
A method for creating a feature-based localization map for a vehicle, including the steps: ascertaining data of at least one object in the surroundings of the vehicle; identifying characteristic structures of the at least one object; combining the characteristic structures to form a simplification structure of the object; and incorporating the simplification structure into the feature-based localization map.