In this research, we present an end-to-end data-driven pipeline for determining the long-term stability status of objects within a given environment, specifically distinguishing between static and dynamic objects. Understanding object stability is key for mobile robots since long-term stable objects can be exploited as landmarks for long-term localisation. Our pipeline includes a labelling method that utilizes historical data from the environment to generate training data for a neural network. Rather than utilizing discrete labels, we propose the use of point-wise continuous label values, indicating the spatio-temporal stability of individual points, to train a point cloud regression network named LTS-NET. Our approach is evaluated on point cloud data from two parking lots in the NCLT dataset, and the results show that our proposed solution, outperforms direct training of a classification model for static vs dynamic object classification.

翻译：本研究提出了一种端到端的数据驱动流程，用于判断给定环境中物体的长期稳定性状态，特别是区分静态物体与动态物体。理解物体稳定性对于移动机器人至关重要，因为长期稳定的物体可作为长期定位的地标。我们的流程包含一种利用环境历史数据生成神经网络训练数据的标注方法。不同于使用离散标签，我们提出采用逐点连续标签值（指示单个点的时空稳定性）来训练名为LTS-NET的点云回归网络。该方法在NCLT数据集两个停车场的点云数据上进行了评估，结果表明我们所提方案优于直接训练分类模型进行静态与动态物体分类的方法。