18092732. NOISE MODELING USING MACHINE LEARNING simplified abstract (GM Cruise Holdings LLC)
Contents
NOISE MODELING USING MACHINE LEARNING
Organization Name
Inventor(s)
Ashish Shrivastava of San Jose CA (US)
Surya Dwarakanath of San Francisco CA (US)
Ignacio Martin Bragado of Mountain View CA (US)
Amin Aghaei of Fremont CA (US)
Ambrish Tyagi of Sunnyvale CA (US)
NOISE MODELING USING MACHINE LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 18092732 titled 'NOISE MODELING USING MACHINE LEARNING
Simplified Explanation: The patent application describes a method for modeling LiDAR noise using machine learning. This involves generating a noisy point cloud to represent real-world environments based on data collected from sensors.
Key Features and Innovation:
- Use of machine learning to generate a noise model for LiDAR data.
- Creation of a noisy point cloud to accurately represent real-world environments.
- Integration of data from simulation environments and real-world environments to improve accuracy.
Potential Applications: This technology can be applied in various industries such as autonomous vehicles, robotics, environmental monitoring, and urban planning.
Problems Solved:
- Improves the accuracy of LiDAR data by modeling noise.
- Enhances the representation of real-world environments in point cloud data.
Benefits:
- Increased precision in LiDAR data analysis.
- Better understanding and representation of real-world environments.
- Improved performance of systems relying on LiDAR technology.
Commercial Applications: The technology can be utilized in autonomous vehicle navigation systems, urban planning tools, environmental monitoring devices, and robotics applications.
Questions about LiDAR Noise Modeling: 1. How does machine learning improve the accuracy of LiDAR noise modeling? 2. What are the potential challenges in implementing this technology in real-world applications?
Frequently Updated Research: Researchers are constantly exploring new algorithms and techniques to enhance LiDAR noise modeling using machine learning. Stay updated on the latest advancements in this field for improved accuracy and efficiency.
Original Abstract Submitted
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.
