  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 a new technique, which we call
continuous clustering. Unlike most existing clustering approaches, which use a
full revolution of the LiDAR sensor, we process the data stream in a continuous
and seamless fashion. Our approach does not rely on the concept of complete or
partial sensor rotations with multiple discrete range images; instead, it views
the range image as a single and infinitely horizontally growing entity. Each
new column of this continuous 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 in order to publish
them without waiting for the completion of the revolution or some other
integration period. In the case of rotating sensors, no problematic
discontinuities between the points of the end and the start of a scan are
observed. In this work we describe the two-layered data structure and the
corresponding algorithm for continuous clustering. It is able to achieve an
average latency of just 5 ms with respect to the latest timestamp of all points
in the cluster. We are publishing the source code at
https://github.com/UniBwTAS/continuous_clustering.
