Localization Framework Parameter Tuning for Multi-Source Vehicle Mapping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional methods for road geometry modeling and environment feature detection are resource-intensive and time-consuming, requiring significant human measurement and calculation, making them costly and impractical for modern applications that involve large data analysis, and existing localization frameworks often need unique configurations for each data source, hindering their functionality.
Innovation Solution
A method that iteratively refines the parameters of a localization framework by applying an offset to map data, recovering offset data, and tuning parameters until correspondence is achieved, allowing for accurate vehicle localization within a mapped environment and updating map data using sensor data from vehicles, which includes constrained and unconstrained parameter tuning and grid-search formulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods for road geometry modeling and environment feature detection are used, then measurement accuracy can be achieved, but the process becomes resource-intensive and time-consuming
Solution Approach 1:
The localization framework performs self-calibration by automatically tuning its own parameters using the sensor data and map data it processes. The system iteratively adjusts parameters to minimize localization errors without requiring external manual calibration, enabling the system to improve its own accuracy while processing data efficiently
Solution Approach 2:
The system dynamically adjusts localization framework parameters based on the specific characteristics of each data source. By changing parameters iteratively during the localization process, the system adapts to different sensor configurations and data qualities, maintaining high accuracy without requiring manual reconfiguration for each scenario
2Measurement precision
If traditional methods with human measurement and calculation are used, then localization accuracy can be achieved, but the cost increases significantly
Solution Approach 1:
The automated parameter tuning eliminates the need for expert operators to manually calibrate the localization framework. The system automatically adjusts its parameters using algorithmic optimization based on the input data, reducing labor costs and making the technology accessible without requiring specialized knowledge
Solution Approach 2:
The system replaces manual human measurement and calculation with automated computational algorithms. The parameter tuning process uses mathematical optimization and iterative computation rather than human expertise, substituting mechanical human labor with automated digital processing
3Measurement precision
If a localization framework is configured for each data source, then localization accuracy for that source is improved, but the device complexity increases
Solution Approach 1:
The localization framework is designed as a universal system that can handle multiple data sources with different sensor configurations. Instead of creating separate frameworks for each source, the single framework adapts its parameters automatically, making it multi-functional and reducing overall system complexity
Solution Approach 2:
The framework parameters are dynamic rather than static, allowing the system to adapt its configuration based on the specific characteristics of each data source being processed. This dynamic adaptation eliminates the need for manual reconfiguration while maintaining optimal performance for different scenarios
4Measurement precision
If manual parameter tuning is performed for each data source, then localization accuracy is improved, but the loss of time increases
Solution Approach 1:
The system performs preliminary parameter initialization based on the characteristics of the data source before actual localization begins. This preliminary configuration provides a good starting point that reduces the number of iterations needed for optimization, saving time while still achieving high accuracy
Solution Approach 2:
The parameter tuning process uses feedback from the localization results to iteratively improve accuracy. The system monitors localization errors and automatically adjusts parameters based on this feedback, eliminating the need for time-consuming manual tuning while achieving optimal performance
Data Source
AI summary
Methods described herein relate to iteratively refining parameters of a localization framework for different data sources. Methods may include: receiving a map data set from a data source; applying an offset to the map data to generate an offset map data set; applying a localization framework between the map data set and the offset map data set to generate a localized data set; recovering offset data from the localized data set; iteratively tuning parameters of the localization framework in response to the offset data from the localized data set failing to correspond with the applied offset until offset data recovered from the localized data set corresponds to the applied offset; receiving sensor data from at least one sensor of a vehicle; applying the localization framework to the sensor data to locate the vehicle within a mapped region.