Vector Semantic Map Matching for Real-Time Vehicle Positioning
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Solution Overview
Problem
Existing vehicle positioning technologies, such as vslam and Islam, require large storage resources due to dense positioning maps and lack descriptors, leading to inefficiencies in real-time high-accuracy matching.
Innovation Solution
A map matching method that utilizes vector semantic maps, determining observation and map semantic features, converting them into a common coordinate system, and matching these features to find optimal candidate positioning information without additional descriptors or intensity data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dense positioning maps with descriptors and intensity information are used, then matching accuracy is improved, but storage resource consumption increases
Solution Approach 1:
The patent extracts only the essential semantic features from the map data, discarding redundant descriptors and intensity information. This selective extraction maintains matching accuracy while significantly reducing storage requirements by keeping only the most critical positioning features.
Solution Approach 2:
The patent uses lightweight vector semantic map representations instead of heavy dense positioning maps. These simplified map structures consume minimal storage resources while providing sufficient information for accurate real-time matching, effectively replacing expensive data structures with cheaper alternatives.
2Quantity of substance
If vector semantic maps with sparsity are used, then storage size is reduced, but matching efficiency and accuracy deteriorate
Solution Approach 1:
The patent transforms the sparsity parameter from a disadvantage into an advantage by designing matching algorithms optimized for sparse vector semantic maps. This parameter change enables efficient real-time matching despite the reduced data density, improving both storage efficiency and matching speed.
Solution Approach 2:
The patent performs preliminary organization and indexing of semantic features in the vector semantic map, preparing the data structure in advance for efficient querying and matching. This preliminary action enables rapid retrieval and comparison even with sparse data, maintaining high matching efficiency.
3Quantity of substance
If vector semantic maps without additional descriptors are used, then storage requirements are reduced, but real-time high-accuracy matching becomes challenging
Solution Approach 1:
The patent introduces semantic features as intermediary elements that bridge the gap between sparse vector map data and accurate positioning requirements. These semantic features act as mediators that enable high-accuracy matching without requiring additional descriptors or intensity information.
Solution Approach 2:
The patent combines multiple types of semantic features (geometric, topological, and contextual) into a composite vector semantic map representation. This composite structure provides rich information for accurate matching while maintaining low storage requirements, as it integrates various feature types into a unified efficient representation.
Data Source
AI summary
A map matching method and apparatus, an electronic device and a storage medium are provided. The method includes: acquiring initial positioning information of a vehicle and vehicle sensor data; determining a plurality of pieces of candidate positioning information on the basis of the initial positioning information; determining a plurality of observation semantic features on the basis of the vehicle sensor data; acquiring local map information on the basis of the initial positioning information of the vehicle, the local map information including a plurality of map semantic features; for each piece of candidate positioning information: converting the plurality of map semantic features into a coordinate system of the vehicle sensor; and matching the plurality of observation semantic features with the plurality of candidate map semantic features; and determining optimal candidate positioning information of the vehicle and a matching pair corresponding to the optimal candidate positioning information.