LiDAR Point Cloud Weather Simulation With Sensor Interference Modeling
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Solution Overview
Problem
Conventional methods fail to accurately simulate realistic atmospheric conditions for sensor measurements, particularly for emissive sensors like LiDAR, due to their dynamic nature and inability to account for interference between multiple sensors, and learning-based techniques are limited to camera-based images.
Innovation Solution
Utilizing machine-learning models to simulate configurable weather conditions in point clouds, including rule-based attenuation and generation of false positives, to accurately model weather effects such as rain, snow, and fog, while removing points below a sensor-specific intensity threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional model-based techniques are used to simulate atmospheric conditions, then the simulation process is simple, but the ability to realistically simulate different weather conditions and accommodate interference between multiple sensors is lacking
Solution Approach 1:
The patent introduces an atmospheric effect model as an intermediary component that receives clear weather point cloud data and sensor configurations, then generates weather-affected point cloud data. This mediator handles the complex transformations required for realistic weather simulation, including attenuation, false positive generation, and intensity modification, while keeping the overall system architecture manageable through modular design
Solution Approach 2:
The patent creates virtual copies of atmospheric conditions by generating synthetic point cloud data that mimics real weather effects. Machine learning models are trained on labeled point cloud data representing different weather conditions, enabling the system to copy and reproduce realistic atmospheric effects without requiring actual physical weather conditions or complex physical modeling
2Adaptability or versatility
If conventional learning-based techniques are used, then the simulation can capture dynamic weather patterns, but the techniques are limited to camera-based images and cannot be applied to point clouds or other sensor measurements
Solution Approach 1:
The patent develops a universal atmospheric effect simulation framework that can process multiple sensor types including LiDAR point clouds, camera images, and other sensor measurements. The system uses sensor-specific configurations and parameters to adapt the same core simulation architecture to different sensor modalities, enabling versatile application across various sensing technologies while maintaining sensor-specific accuracy through customized attenuation models and intensity thresholds
3Reliability
If machine learning models are used to simulate atmospheric conditions in point clouds, then realistic weather effects can be achieved, but computational complexity and processing time increase
Solution Approach 1:
The patent performs preliminary actions by pre-training machine learning models on extensive labeled point cloud datasets representing various weather conditions. Once trained, these models can rapidly generate realistic weather effects during actual simulation or deployment. The heavy computational work of learning weather patterns is done in advance, allowing faster inference time when generating weather-affected point cloud data from clear weather inputs
Data Source
AI summary
In various examples, systems and methods are disclosed relating to transforming sensor measurements according to configurable atmospheric conditions. One or more circuits can identify a point cloud comprising a plurality of points and a parameter of a weather condition to simulate and modify an intensity of at least one of the plurality of points according to the parameter of the weather condition. The one or more circuits can update, based at least on a subset of the plurality of points and the parameter of the weather condition, the point cloud to include one or more additional points.