Simulating Sensor Noise Characteristics for Robot Training
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Solution Overview
Problem
Current robotic simulation systems struggle to accurately replicate real-world sensor data noise, leading to a gap between virtual and real-world training data, which hinders the effectiveness of training autonomous devices like robots and self-driving cars.
Innovation Solution
The use of convolutional neural networks to analyze real sensor data and simulate noise-adjusted sensor data by extracting noise characteristics from real sensor data and applying them to ground-truth data generated in virtual environments, ensuring the simulated data closely resembles real-world conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If ground-truth data is generated in virtual environment without noise, then manufacturing precision of simulated data is improved, but reliability of training for real-world operation deteriorates
Solution Approach 1:
The patent introduces noise characteristics extracted from real sensor data as an intermediary element. This noise characteristic acts as a mediator between the clean ground-truth data and the noisy real-world sensor data, allowing the simulated data to bridge the gap between virtual precision and real-world reliability.
Solution Approach 2:
The patent changes the parameter of the simulated sensor data by adding noise characteristics extracted from real sensor data. This parameter change transforms the clean ground-truth data into noise-adjusted simulated sensor data that better reflects real-world conditions while maintaining the structural accuracy of the ground-truth data.
2Reliability
If real sensor data with noise is used directly for training, then reliability of training is improved, but manufacturing precision and control of training data deteriorates
Solution Approach 1:
The patent extracts only the noise characteristics from real sensor data while separating them from the ground-truth data. This extraction allows the noise to be applied selectively and controllably to simulated data, maintaining precision control while incorporating real-world reliability factors.
Solution Approach 2:
The patent creates a copy of the noise characteristics from real sensor data and applies this copied noise pattern to the ground-truth data. This copying approach allows the simulated data to replicate real-world noise patterns without using the actual messy real sensor data directly, maintaining control over the training data quality.
3Reliability
If noise is added to ground-truth data to match real sensor characteristics, then reliability of training is improved, but manufacturing precision of simulated data deteriorates
Solution Approach 1:
The patent applies noise characteristics locally and selectively to the ground-truth data rather than uniformly altering it. The noise adjustment is applied in a controlled manner to specific aspects of the data, preserving the high-quality ground-truth information while adding localized realism through noise characteristics.
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture to simulate sensor data are disclosed. An example apparatus includes a noise characteristic identifier to extract a noise characteristic associated with a feature present in first sensor data obtained by a physical sensor. A feature identifier is to identify a feature present in second sensor data. The second sensor data is generated by an environment simulator simulating a virtual representation of the real sensor. A noise simulator is to synthesize noise-adjusted simulated sensor data based on the feature identified in the second sensor data and the noise characteristic associated with the feature present in the first sensor data.