Vehicle Localization Using Static Object Pattern Filtering
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Solution Overview
Problem
Existing methods for localizing and mapping vehicles in environments, such as SLAM algorithms, face challenges in maintaining accuracy due to slight environmental changes like snowfall, growing grass, or falling leaves, which can result in localization errors.
Innovation Solution
A method that uses environment perception sensors to identify and filter measurements based on predefined patterns of static objects offset from each other, allowing for more robust and reliable localization and mapping by disregarding minor environmental changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If SLAM algorithms are used for localization and mapping, then the vehicle can be localized in the environment, but localization accuracy deteriorates due to slight environmental changes like snowfall, growing grass, or falling leaves
Solution Approach 1:
The patent segments the environment into predefined patterns of static objects (such as poles, signs, or other structured elements) that are offset from each other in a specific configuration. By focusing only on these segmented, pattern-matched objects rather than the entire environment, the system filters out irrelevant changes like snowfall or falling leaves, thereby maintaining localization accuracy despite environmental variations.
2Measurement precision
If environment perception sensors continuously obtain measurements for localization and mapping, then the vehicle position can be obtained, but processing power requirements increase
Solution Approach 1:
The patent extracts and filters only those measurements that correspond to predefined patterns of static objects with specific offset relationships. By taking out only the relevant pattern-matched objects from the continuous sensor measurements and discarding other data, the system reduces the processing load while maintaining measurement precision for vehicle localization.
3Quantity of substance
If all objects in the environment are used for localization, then more data is available for mapping, but localization errors increase due to moving objects and environmental changes
Solution Approach 1:
The patent applies local quality by assigning different properties to different objects based on their characteristics. Predefined patterns of static objects with specific offset relationships are identified as suitable for localization, while other objects (moving objects, transient elements) are excluded. This selective approach ensures that only reliable, stable objects contribute to localization, improving accuracy while maintaining a sufficient quantity of reference objects.
Data Source
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AI summary
The invention relates to a method for localizing and/or mapping during operation of a vehicle (100) in an environment, wherein the vehicle (100) comprises at least one environment perception sensor (110) for the localizing and/or mapping, the method comprising: - obtaining (S1) at least one measurement of the environment from the environment perception sensor (110), - searching for and identifying (S2) a plurality of objects (10, 20, 30) in the at least one measurement which correspond to a predefined pattern, - in response to determining that the plurality of objects correspond to the predefined pattern, filtering (S3) the at least one measurement so that only the plurality of objects which correspond to the predefined pattern are used for the localizing and/or mapping, and - localizing and/or mapping (S4) based on the filtered at least one measurement. The invention also relates to a control unit (120), a vehicle (100), a computer program and a computer readable medium.