Semantic Segmentation Network for Warehouse Indoor Environments from 3D LiDAR Point Clouds - Martins, Gabriel do Pinho; Kappel, Kristofer Stift; Garcia, Vinicius Leite de; Oliveira, Felipe Gomes de; Duarte Filho, Nelson; Drews, Paulo Lilles Jorge (Anais do Simpósio Brasileiro de Robótica e Simpósio Latino Americano de Robótica (SBR/LARS), 2025)
Robust perception is paramount for autonomous systems that operate in real public environments. LiDAR sensors are known for their robustness, making them ideal for autonomous robots, which enable obstacle detection, reliability in adverse lighting conditions, and overall safety assurance. Most 3D LiDAR semantic segmentation networks are fine-tuned for outdoor and urban environments, overlooking other equally important scenes, such as industrial scenarios. In this work, we contribute to an open-source Convolutional Neural Network (CNN) pipeline for semantic segmentation of 3D LiDAR point clouds of indoor warehouse environments. Additionally, we introduce a dataset of simulated industrial point clouds for 3D semantic segmentation called WareSet. We evaluate the quality and viability of our network using well-defined quality metrics, including accuracy, Intersection over Union (IoU) per class, and mean Intersection over Union (mIoU), for both training and validation sets. The experiments were conducted in an unfamiliar warehouse environment, taking into account the proposed dataset and the specified quality metrics. The proposed approach yields satisfactory results of 98.49% and 90.59% for accuracy, and 90.64% and 49.49% for mIoU in training and validation, respectively, with good model convergence. Additionally, it achieves 89.7% accuracy and 50.4% mIoU on the unknown warehouse, emphasizing its generalization capability. The proposed dataset is open-sourced at https://github.com/DeskFanzin/WareSet .