The ability to predict future structure features of environments based on past perception information is extremely needed by autonomous vehicles, which helps to make the following decision-making and path planning more reasonable. Recently, point cloud prediction (PCP) is utilized to predict and describe future environmental structures by the point cloud form. In this letter, we propose a novel efficient Transformer-based network to predict the future LiDAR point clouds exploiting the past point cloud sequences. We also design a semantic auxiliary training strategy to make the predicted LiDAR point cloud sequence semantically similar to the ground truth and thus improves the significance of the deployment for more tasks in real-vehicle applications. Our approach is completely self-supervised, which means it does not require any manual labeling and has a solid generalization ability toward different environments. The experimental results show that our method outperforms the state-of-the-art PCP methods on the prediction results and semantic similarity, and has a good real-time performance. Our open-source code and pre-trained models are available at https://github.com/Blurryface0814/PCPNet.
翻译:能够根据过去的感知信息预测环境未来的结构特征,对于自动驾驶汽车具有非常重要的意义,有助于使后续的决策制定和路径规划更加合理。最近,点云预测(PCP)被用于通过点云形式预测和描述未来的环境结构。本文提出了一种新颖的高效变压器网络,利用过去的点云序列来预测未来的LiDAR点云。我们还设计了一个语义辅助训练策略,使预测的LiDAR点云序列在语义上与真实情况相似,从而提高了在真实车辆应用中更多任务部署的意义。我们的方法完全是自监督的,意味着不需要任何手动标注,并具有较好的不同环境的泛化能力。实验结果表明,我们的方法在预测结果和语义相似性方面优于现有的PCP方法,具有很好的实时性能。我们的开源代码和预训练模型可在https://github.com/Blurryface0814/PCPNet上获得。