# End-to-end sensor modeling for LiDAR Point Cloud

## TL;DR

This paper introduces a deep learning-based LiDAR sensor model that accurately simulates sensor echoes using Polar Grid Maps, reducing the need for costly real-world data and aiding virtual testing environments.

## Contribution

It presents a novel deep neural network approach for modeling LiDAR sensor echoes, improving virtual testing and reducing data annotation costs.

## Key findings

- Model achieves promising benchmark results with real sensor data.
- Sets a baseline for future LiDAR sensor modeling research.
- Improves virtual testing environments for self-driving cars.

## Abstract

Advanced sensors are a key to enable self-driving cars technology. Laser scanner sensors (LiDAR, Light Detection And Ranging) became a fundamental choice due to its long-range and robustness to low light driving conditions. The problem of designing a control software for self-driving cars is a complex task to explicitly formulate in rule-based systems, thus recent approaches rely on machine learning that can learn those rules from data. The major problem with such approaches is that the amount of training data required for generalizing a machine learning model is big, and on the other hand LiDAR data annotation is very costly compared to other car sensors. An accurate LiDAR sensor model can cope with such problem. Moreover, its value goes beyond this because existing LiDAR development, validation, and evaluation platforms and processes are very costly, and virtual testing and development environments are still immature in terms of physical properties representation. In this work we propose a novel Deep Learning-based LiDAR sensor model. This method models the sensor echos, using a Deep Neural Network to model echo pulse widths learned from real data using Polar Grid Maps (PGM). We benchmark our model performance against comprehensive real sensor data and very promising results are achieved that sets a baseline for future works.