Synthetic LiDAR-Labeled Data Set Generation to Train Deep Neural Networks for Object Classification in IoT at the Edge

Abstract

LiDAR sensors are increasing in popularity due to the advantages they provide over 2D sensors in IoT object detection and classification applications, because of their ability to provide very precise distances to objects. Deep learning algorithms need a huge amount of data during training to obtain high accuracy results. When using 2D images a vast quantity of datasets are publicly available, but this is not the case for LiDAR point clouds. Each LiDAR model generates a point cloud with unique properties, which causes the datasets not to be compatible between different LiDAR models. As a result, when using deep learning with LiDARs it is necessary to generate the datasets manually. For this purpose, the data must be captured and then labeled one by one, which is a very time and cost consuming process. To overcome this issue and to reduce the development time when using LiDAR sensors with deep learning algorithms, a methodology is proposed in this paper to automatically generate point cloud datasets using a 3D simulator for autonomous cars. In this regard, a dataset can be generated for any LiDAR model by adding the specific LiDAR parameters to the simulator. Besides, custom scenarios can be designed and generated, based on the final deployment location, to provide a simulated solution very close to the final implementation. With the proposed methodology, a simulation can be performed to select the LiDAR that best fits certain application requirements, in contrast to the traditional approach where the LiDAR must first be purchased.