Abstract: Systems and methods of simulating a LiDAR return signal are disclosed. The method includes the creation of a model of an object and a LiDAR unit in a virtual environment. The LiDAR return signal includes the return intensity of a reflection of an incident illumination beam by the object. The model determines the LiDAR return signal based in part on a range from the LiDAR unit to the object and an object label. The model is trained with a set of road data records each having a measured range, a measured return intensity, and an object label. The trained model simulates a LiDAR illumination beam emitted by the LiDAR unit toward the object and determines the return intensity of the reflection of an incident portion of the emitted illumination beam.

Abstract: 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.

Abstract: 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.