Autonomous Vehicle Localization Using Dense LIDAR Ground Truth
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Solution Overview
Problem
Existing localization systems for autonomous vehicles face challenges in achieving accurate and diverse ground truth datasets, which are essential for reliable localization, due to high costs, manual annotation difficulties, and inaccuracies from geolocation systems and structure-from-motion models.
Innovation Solution
A method for generating a ground truth dataset using dense scans and vehicle dynamics, combined with LIDAR registration, to create a comprehensive and granularly labeled dataset that covers a large area, enabling accurate real-time localization with machine-learned retrieval models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If geolocation systems and structure-from-motion models are used for localization, then localization functionality is provided, but measurement precision and reliability deteriorate due to inherent inaccuracies
Solution Approach 1:
The patent creates a digital copy of the physical environment through dense LIDAR scans to form a ground truth dataset. This digital replica serves as a reference map that the retrieval model compares against current sensor readings, replacing unreliable geolocation and structure-from-motion methods with a more accurate digital twin approach for localization.
Solution Approach 2:
The patent performs preliminary actions by pre-collecting dense LIDAR scans and pre-localizing sensor observations to create a comprehensive ground truth dataset before actual localization operations. This advance preparation of reference data enables accurate real-time localization without relying on less precise geolocation or structure-from-motion computations during operation.
2Manufacturing precision
If manual annotation methods are used to create ground truth datasets, then dataset accuracy can be improved, but productivity and ease of manufacture deteriorate due to high costs and difficulties
Solution Approach 1:
The patent implements self-service by using automated LIDAR-based dense scanning and algorithmic pre-localization of sensor observations to create the ground truth dataset. This eliminates the need for manual annotation while maintaining high accuracy, as the system automatically generates and localizes the reference data through computational processes rather than human labor.
Solution Approach 2:
The patent replaces the mechanical process of manual annotation with an automated optical and computational system. LIDAR sensors capture environmental data, and algorithms automatically process and localize sensor observations to create the ground truth dataset, substituting human manual work with automated sensing and computation systems that achieve both accuracy and efficiency.
3Adaptability or versatility
If diverse and large-scale ground truth datasets are created, then localization accuracy and adaptability improve, but device complexity and resource requirements increase
Solution Approach 1:
The patent segments the ground truth dataset into discrete pre-localized sensor observations, each with associated pose values. This segmentation allows the retrieval model to efficiently query and compare individual observations rather than processing entire datasets, reducing computational complexity while maintaining the benefits of diverse, large-scale training data for improved adaptability.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach facilitates the creation of a dataset with sufficient scale, diversity, and accuracy for autonomous vehicle localization, improving localization accuracy to within one meter and enabling comprehensive testing of localization algorithms.
Implementation Method 1
obtaining a current sensor reading representation from one or more sensors located at the vehicle
Data Source
AI summary
A computer-implemented method for localizing a vehicle can include accessing, by a computing system comprising one or more computing devices, a machine-learned retrieval model that has been trained using a ground truth dataset comprising a plurality of pre-localized sensor observations. Each of the plurality of pre-localized sensor observations has a predetermined pose value associated with a previously obtained sensor reading representation. The method also includes obtaining, by the computing system, a current sensor reading representation obtained by one or more sensors located at the vehicle. The method also includes inputting, by the computing system, the current sensor reading representation into the machine-learned retrieval model. The method also includes receiving, by the computing system and from the machine-learned retrieval model, a determined current pose value for the vehicle based at least in part on one or more of the pre-localized sensor observations determined to be a closest match to the current sensor reading representation. The determined current pose value has an accuracy of within about one meter.