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

• Published in: Internet of Things Journal, IEEE Vol. 9; pp. 24812 - 24821
• Main Authors: Wisultschew, Cristian, Hernandez, Rogelio, Pastor, Carlos, Portilla, Jorge
• Format: Standard
• Language: English
• Published: IEEE 15-12-2022
• Subjects: 3-D simulator; Deep learning; deep neural networks (DNNs); Laser radar; light detection and ranging (LiDAR); object classification; Object detection; Sensors; simulated data set; Training
• DOI: 10.1109/JIOT.2022.3194716

Light detection and ranging (LiDAR) sensors are increasing in popularity due to the advantages they provide over 2-D 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 2-D images, a vast quantity of data sets 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 data sets not to be compatible between different LiDAR models. As a result, when using deep learning with LiDARs, it is necessary to generate the data sets 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 article to automatically generate point cloud data sets using a 3-D simulator for autonomous cars. In this regard, a data set 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.