Local Transform Propagation for Lane Divider Map Alignment
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Solution Overview
Problem
Existing systems face challenges in accurately matching and aligning map data elements, particularly lane dividers and road boundaries, due to sensor imprecision and varying vehicle trajectories, making it difficult to determine precise positions and generate accurate digital environments for autonomous navigation.
Innovation Solution
The approach involves identifying seed area candidates with high landmark density, using local alignment tracking to establish initial transforms for lane dividers, and iteratively refining these transforms to propagate accurate matches along road segments, employing optimization techniques to align tracks and generate object-based representations for map generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple passes of sensor data are captured for each roadway section, then accuracy of map data is improved, but time consumption and data processing complexity increase
Solution Approach 1:
The system performs preliminary actions by capturing sensor data from multiple passes in advance and storing it in a database. This allows the mapping system to process and align this pre-captured data later to generate accurate map representations, thereby improving map data accuracy without requiring real-time multiple passes during actual mapping operations.
Solution Approach 2:
The patent segments the map generation process into distinct phases: data capture from multiple passes, data storage, feature extraction, track alignment, and map representation generation. This segmentation allows each phase to be optimized independently, reducing overall time consumption while maintaining accuracy requirements.
2Measurement precision
If multiple passes of sensor data are captured for each roadway section, then accuracy of map data is improved, but data processing complexity increases
Solution Approach 1:
The patent divides the complex data processing task into manageable segments: sensor data capture, feature extraction from captured data, track alignment using extracted features, and final map representation generation. This segmentation reduces processing complexity by handling one aspect at a time rather than processing all data simultaneously.
Solution Approach 2:
The system introduces intermediate structures such as feature extractions and track alignments that serve as mediators between raw sensor data and final map representations. These intermediaries simplify the processing by creating structured, organized data that is easier to work with in subsequent processing stages.
3Stability of the object's composition
If features such as road boundaries and lane dividers are tracked along significant distances, then continuity of map representation is improved, but difficulty in determining precise positions increases
Solution Approach 1:
The system employs feedback mechanisms by continuously comparing extracted features from multiple tracks and using alignment algorithms to adjust and refine feature positions. This feedback loop ensures that while maintaining continuity over long distances, the system can correct positional drift and maintain precision through iterative refinement.
Solution Approach 2:
The patent performs preliminary feature extraction and track alignment before final map representation generation. By pre-processing the data to establish rough alignments and identify key features, the system reduces the complexity of determining precise positions later, maintaining both continuity and accuracy.
4Reliability
If sensor imprecision and varying vehicle trajectories are accounted for, then reliability of map data is improved, but measurement and alignment difficulty increases
Solution Approach 1:
The patent segments the alignment process into distinct steps: initial feature extraction from sensor data, preliminary track alignment based on extracted features, refinement of alignments using multiple tracks, and final verification. This segmentation makes the complex alignment task more manageable and reduces overall difficulty.
Solution Approach 2:
The system introduces intermediate alignment structures and feature matches that act as mediators between sensor data from different tracks. These intermediaries provide a structured framework for handling imprecision and trajectory variations, making the alignment process more systematic and less difficult.
Data Source
AI summary
Approaches presented herein provide for the matching and alignment of features in different instances of sensor data corresponding to an environment. At least one embodiment provides for accurate identification of matching lane dividers between two or more tracks obtained from sensor-equipped vehicles or machines. An initial transform can be determined using a seed area for tracks of data, where the seed area can be determined using landmarks, lane boundaries, or other such objects identified from the sensor data. The initial transform can be used to determine lane divider matches in the track data. If successfully evaluated, these lane divider matches from the seed areas can be propagated out in one or more tracking directions along a roadway to determine lane divider matches along entire stretches of roadway, including roads that pass through intersections or other relatively complex regions.