Descriptor-Based Vehicle Map Positioning in Semantic Road Maps
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Solution Overview
Problem
Existing methods for positioning a vehicle's surroundings in a semantic road map lack precision and efficiency, particularly in updating and integrating detailed surroundings data.
Innovation Solution
A method involving the creation of a map representation using surrounding sensor data, comparison of characteristic elements with a semantic road map, and utilization of descriptors like neighbor binary landmark descriptors and RANSAC algorithm for precise alignment and integration of surroundings data into the semantic road map.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If characteristic elements are compared directly without descriptors, then the comparison process is simpler, but positioning precision deteriorates
Solution Approach 1:
Descriptors serve as intermediary representations of characteristic elements. Instead of directly comparing raw characteristic elements between map representation and semantic road map, the system extracts descriptors (feature vectors capturing geometric and semantic properties) as intermediate forms. This intermediary step enables precise comparison while maintaining manageable complexity, as descriptors standardize the comparison format and highlight key distinguishing features.
Solution Approach 2:
The system transforms characteristic elements into descriptors by changing their representation parameters. Descriptors encode multiple parameters (position, orientation, shape, semantic class) of characteristic elements into standardized feature vectors. This parameter transformation enables quantitative comparison and precise matching between map representation and semantic road map elements.
2Loss of information
If the map representation covers the entire surroundings, then completeness is improved, but processing time and computational load increase
Solution Approach 1:
The system segments the surroundings into multiple map representations, each covering a specific field of view or region. Instead of processing one large comprehensive map, the system divides the environment into manageable segments (e.g., based on sensor fields of view or geographic regions). Each segment is processed independently for positioning, reducing computational load per task while maintaining overall completeness through aggregation of multiple segments.
Solution Approach 2:
The system performs partial positioning actions by comparing only relevant characteristic elements within each map representation against the semantic road map, rather than processing all possible elements. By focusing computation on salient characteristic elements (road markings, signs, buildings) within each segment, the system achieves sufficient positioning accuracy without exhaustive processing of all surroundings data.
3Measurement precision
If more characteristic elements are used for positioning, then positioning accuracy is improved, but the complexity of identifying and comparing elements increases
Solution Approach 1:
The system applies different processing qualities to different characteristic elements based on their importance and reliability for positioning. Not all characteristic elements are treated equally - the system identifies and prioritizes high-value elements (such as unique road signs, distinctive buildings, or well-defined road markings) that provide strong positioning signals. Less important elements are processed with simpler methods or given lower weight in the positioning calculation, optimizing the accuracy-complexity tradeoff.
Data Source
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AI summary
The invention relates to a method (100) for positioning a map display (200) of surroundings (501) of a vehicle (500) in a semantic road map (300), comprising: - receiving (101) surroundings sensor data (505) from at least one surroundings sensor (503) of a vehicle (500); - creating (103) a map display (200) of surroundings (501) of the vehicle (500) based on the surroundings sensor data (505) of the vehicle (500); - determining (105) characteristic elements (201) in the map display (200) and characteristic elements (301) in a semantic road map (300) depicting the surroundings (501); - comparing (107) the characteristic elements (201) of the map display (200) with the characteristic elements (301) of the semantic road map (300); - determining (109) a section (303) of the semantic road map (300) for which at least one characteristic element (301) of the section (303) of the semantic road map (300) matches at least one characteristic element (201) of the map display (200) and for which the map display (200) and the semantic road map (300) describe an identical region of the surroundings (501) of the vehicle (500); and - identifying (111) a position (P) of the section (303) within the semantic road map (300) as position of the map display (200) in the semantic road map (300).