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
this https URL

低延迟的实例分割是机器人感知流水线中的一个重要组成部分，本文介绍了一种利用连续聚类处理激光雷达点云数据以实现实时实例分割的方法，并描述了其数据结构、算法和重要的架构设计选择。