Low-latency instance segmentation of LiDAR point clouds is crucial in real-world applications because it serves as an initial and frequently-used building block in a robot's perception pipeline, where every task adds further delay. Particularly in dynamic environments, this total delay can result in significant positional offsets of dynamic objects, as seen in highway scenarios. To address this issue, we employ continuous clustering of obstacle points in order to obtain an instance-segmented point cloud. Unlike most existing approaches, which use a full revolution of the LiDAR sensor, we process the data stream in a continuous and seamless fashion. More specifically, each column of a range image is processed as soon it is available. Obstacle points are clustered to existing instances in real-time and it is checked at a high-frequency which instances are completed and are ready to be published. An additional advantage is that no problematic discontinuities between the points of the start and the end of a scan are observed. In this work we describe the two-layered data structure and the corresponding algorithm for continuous clustering, which is able to cluster the incoming data in real time. We explain the importance of a large perceptive field of view. Furthermore, we describe and evaluate important architectural design choices, which could be relevant to design an architecture for deep learning based low-latency instance segmentation. We are publishing the source code at https://github.com/UniBwTAS/continuous_clustering.

翻译：LiDAR点云的低延迟实例分割在实际应用中至关重要，因为它作为机器人感知流水线中初始且频繁使用的基础模块，每个任务都会增加额外延迟。特别是在动态环境中（如高速公路场景），这种总延迟可能导致动态对象显著的位置偏移。为解决该问题，我们采用对障碍物点进行连续聚类的方法，以获得实例分割后的点云。与多数使用LiDAR传感器完整旋转周期的现有方法不同，我们以连续无缝的方式处理数据流。具体而言，每个距离图像列在其可用时立即被处理。障碍物点实时聚类到现有实例中，并以高频检查哪些实例已完成并准备发布。额外优势在于，扫描起始与结束点之间不会出现有问题的非连续性。本文描述了用于连续聚类的双层数据结构及对应算法，该算法能实时聚类输入数据。我们论证了大视场感知的重要性，并进一步描述和评估了重要的架构设计选择，这些选择可能对设计基于深度学习的低延迟实例分割架构具有参考价值。源代码已发布在 https://github.com/UniBwTAS/continuous_clustering。