Simulation-Trained Neural Networks for Corrupted Sensor Data
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Solution Overview
Problem
Autonomous devices face challenges in accurately processing sensor data due to corruption, leading to poor task performance, as existing methods like sensor localization struggle with diverse datasets from multiple sensors.
Innovation Solution
A system utilizing neural networks trained with synthetic data from simulations and ground-truth data to correct and adjust corrupted sensor data, incorporating sensor localization techniques for more robust data recovery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor localization is used to correct corrupted data, then data recovery capability is improved, but performance degrades when handling diverse datasets from multiple sensors
Solution Approach 1:
The patent transforms the sensor localization problem into a data transformation problem by changing parameters through neural network-based simulations. Instead of relying on geometric localization that fails with diverse sensors, the system uses neural networks to learn and transform corrupted sensor data into corrected representations, enabling robust performance across multiple sensor types and environments
Solution Approach 2:
The patent introduces neural network simulations as an intermediary between raw sensor data and task execution. The neural networks act as a mediator that processes diverse sensor inputs, learns from ground truth data, and generates corrected sensor representations, thereby resolving the adaptability issue across different sensor configurations
2Measurement precision
If neural networks are trained with synthetic data from simulations, then accuracy of corrected data is improved, but computational resources and training time are increased
Solution Approach 1:
The patent applies preliminary action by pre-training neural networks with synthetic data from simulations before deploying to correct real sensor data. The neural networks are trained in advance using simulated environments that replicate various sensor conditions, allowing them to accurately correct corrupted data in production without requiring continuous intensive computational resources
Solution Approach 2:
The patent uses synthetic data as a copy of real sensor data to train neural networks. By creating virtual replicas of sensor scenarios through simulation, the system can train models on large volumes of labeled data without the computational burden of collecting and processing equivalent real-world data, thereby reducing overall resource consumption
Data Source
AI summary
Apparatuses, systems, and techniques to adjust data using simulation(s) of said data. In at least one embodiment, one or more neural networks are used, or otherwise caused, to use first sensor information from one or more simulations of an autonomous device to adjust second sensor information of said autonomous device.