Localization Framework Parameter Tuning for Autonomous Navigation
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Solution Overview
Problem
Traditional road geometry modeling and object identification methods are resource-intensive and time-consuming, requiring significant human effort and calculation, and existing localization frameworks need customization for each data source, making them impractical for large-scale data analysis in crowd-sourced scenarios.
Innovation Solution
A method and apparatus that identify and tune a subset of parameters in a localization framework based on the data source by comparing received data sets against stored data, using a data similarity module to determine critical parameters for accurate localization and map updates, allowing for efficient processing and integration of sensor data into mapped regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional road geometry modeling and object identification methods are used, then accuracy of localization is improved, but resource consumption and time required increase significantly
Solution Approach 1:
The patent extracts and identifies only the critical subset of parameters from the complete localization framework that actually impact localization accuracy for a given data source. By taking out only the essential parameters rather than tuning all parameters, the system achieves accurate localization while significantly reducing computational resources and time required for parameter tuning.
Solution Approach 2:
The patent segments the parameter tuning process into two distinct phases: (1) automated identification of the critical parameter subset based on data source characteristics, and (2) tuning only those identified critical parameters. This segmentation transforms a monolithic, resource-intensive tuning process into a streamlined two-stage approach that maintains accuracy while improving efficiency.
2Measurement precision
If a localization framework is customized for each data source, then localization accuracy for that data source is improved, but device complexity and implementation cost increase
Solution Approach 1:
The patent changes the approach from structural customization of the localization framework to parameter-based adaptation. By identifying and tuning only the critical parameters specific to each data source rather than customizing the entire framework structure, the system achieves data-source-specific optimization while keeping the overall framework unified and manageable.
Solution Approach 2:
The patent creates a universal parameter identification mechanism that can handle multiple different data sources through a single automated process. The system universally identifies critical parameters based on data source characteristics without requiring separate customization procedures for each data source type, thereby reducing implementation complexity while maintaining adaptability.
3Measurement precision
If all parameters of the localization framework are tuned, then localization accuracy is improved, but time and computational resources required increase
Solution Approach 1:
The patent extracts only the critical parameters that genuinely impact localization accuracy for each data source, eliminating the need to tune non-critical parameters. This extraction process, automated through data source analysis, dramatically reduces the number of parameters requiring manual or computational tuning, thereby reducing time and resource investment.
Solution Approach 2:
The patent applies partial action by tuning only the necessary critical parameters rather than all parameters in the localization framework. This partial tuning approach is sufficient to achieve accurate localization while avoiding the excessive time and computational resources that would be required to tune the complete parameter set.
Data Source
AI summary
Methods described herein relate to identifying critical parameters of a localization framework that may require tuning for accuracy of the localization framework based on the data source. Methods may include: receiving a data set from a data source; comparing the data set against data stored in a database; identifying, based on the comparison, a subset of parameters of a localization framework for the data set from among a plurality of parameters of the localization framework; providing for tuning of the subset of parameters of the localization framework for the data set to generate a tuned subset of parameters; processing the data set from the data source using the localization framework including the tuned subset of parameters; and receiving an indication of a location from the localization framework of the data set within a mapped region.