Data Augmentation for Safe 3D Object Detection for Autonomous Volvo Construction Vehicles

Abstract: Point cloud data can express the 3D features of objects, and is an important data type in the field of 3D object detection. Since point cloud data is more difficult to collect than image data and the scale of existing datasets is smaller, point cloud data augmentation is introduced to allow more features to be discovered on existing data. In this thesis, we propose a novel method to enhance the point cloud scene, based on the generative adversarial network (GAN) to realize the augmentation of the objects and then integrate them into the existing scenes. A good fidelity and coverage are achieved between the fake sample and the real sample, with JSD equal to 0.027, MMD equal to 0.00064, and coverage equal to 0.376. In addition, we investigated the functional data annotation tools and completed the data labeling task. The 3D object detection task is carried out on the point cloud data, and we have achieved a relatively good detection results in a short processing of around 22ms. Quantitative and qualitative analysis is carried out on different models.