Initial Ego-Pose Localization Using Clustered Particle Filtering
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Solution Overview
Problem
Existing self-localization methods for autonomous vehicles face challenges in determining an initial ego-pose when Global Navigation Satellite Systems (GNSS) signals are not available, leading to inefficiencies and unreliability, especially in situations with noisy and sparse measurements.
Innovation Solution
A computer-implemented method using particle filtering with clustering techniques, where particles are grouped into clusters, and their mean values and variances are updated to track the vehicle's location efficiently, allowing for systematic particle injection based on cluster tracking and sensor data, even when particles are no longer present, to maintain accurate localization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If particle filtering is used for global self-localization when GNSS signals are not available, then the initial ego-pose can be determined, but the processing effort and computational complexity increase significantly
Solution Approach 1:
The patent divides the particle set into multiple clusters, where each cluster represents a potential ego-pose hypothesis. This segmentation allows the system to track multiple hypotheses in parallel, improving reliability of localization while managing computational complexity through organized structure.
Solution Approach 2:
The system performs preliminary clustering of particles into groups representing different ego-pose hypotheses before full processing. By pre-organizing particles into clusters with representative mean values and variances, the system reduces the computational burden during actual filtering operations.
2Measurement precision
If particles are systematically injected based on cluster tracking, then filter divergence is avoided and localization accuracy is maintained, but the data management complexity increases
Solution Approach 1:
The system continuously monitors cluster characteristics (mean value and variance) and uses this feedback to determine when and where to inject particles. This feedback mechanism ensures that particle injection occurs only when necessary to maintain localization accuracy, avoiding unnecessary computational overhead.
Solution Approach 2:
The patent changes the parameter representation from tracking individual particles to tracking cluster-level parameters (mean value and variance). This parameter transformation simplifies data management while maintaining the ability to detect filter divergence and trigger particle injection when needed.
3Productivity
If the mean value and variance of clusters are updated continuously, then efficient tracking of particle locations is achieved, but the processing effort increases
Solution Approach 1:
Instead of tracking every individual particle, the system creates simplified copies represented by cluster mean values and variances. These statistical copies capture the essential location information of particle clusters while requiring minimal processing effort to update and maintain.
Data Source
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AI summary
A computer implemented method for determining an initial ego-pose for initialization of self-localization comprises the following steps carried out by computer hardware components: providing a plurality of particles in a map; grouping the particles in a plurality of clusters, each cluster comprising a respective subset of the plurality of particles; during particle filtering, injecting particles based on the plurality of clusters; and determining an initial ego-pose based on the particle filtering.