LiDAR Noise Modeling Using Machine Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles (AVs) face challenges in accurately modeling real-world noise in sensor data, particularly with LiDAR systems, which can result in inaccuracies in point clouds due to noise from various parameters.
Innovation Solution
The use of machine learning, specifically deep learning neural networks, to develop a noise model that can be combined with point clouds from simulation environments to simulate the noise characteristics of real-world environments, thereby improving the accuracy of AV navigation and routing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional noise modeling methods are used in simulation environments, then the computational process is simpler, but the accuracy of representing real-world noise characteristics deteriorates
Solution Approach 1:
The patent creates a digital copy of real-world noise characteristics by training a neural network on actual sensor data collected from real environments. The network learns to replicate the statistical properties and patterns of real noise, generating synthetic noise that mirrors real-world conditions without requiring physical real-world data in every simulation run.
Solution Approach 2:
The neural network model transforms the representation of noise by changing parameters from simple random generation to learned statistical distributions. The network adjusts noise parameters dynamically based on environmental conditions, transforming the noise modeling from static to adaptive parameter control.
2Reliability
If real-world sensor data is collected and processed, then the noise model accuracy is improved, but the data processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary action by collecting and processing real-world sensor data beforehand to train the neural network model. This training phase is conducted offline, allowing the system to learn noise characteristics in advance. During actual simulation runs, the pre-trained network generates noise rapidly without requiring real-time processing of raw sensor data.
Solution Approach 2:
The trained neural network enables continuous generation of realistic noise during simulations without interruption for data processing. The model maintains continuous operation by generating noise on-demand based on learned patterns, eliminating gaps where real-time data collection and processing would otherwise be required.
Data Source
AI summary
Systems and techniques are described for Light Detection and Ranging (LiDAR) noise modeling using machine learning (ML). An example method can include collecting, using one or more sensors, a first set of data for a simulation environment and generating, using the first set of data, a point cloud that represents and/or describes the simulation environment. The method can further include collecting, using the one or more sensors, a second set of data for a real-world environment and generating a noise model using the second set of data and a neural network. The method can also include generating, using the noise model and the point cloud, a noisy point cloud that represents and/or describes the real-world environment.