  Many modern robotics systems employ LiDAR as their main sensing modality due
to its geometrical richness. Rolling shutter LiDARs are particularly common, in
which an array of lasers scans the scene from a rotating base. Points are
emitted as a stream of packets, each covering a sector of the 360{\deg}
coverage. Modern perception algorithms wait for the full sweep to be built
before processing the data, which introduces an additional latency. For typical
10Hz LiDARs this will be 100ms. As a consequence, by the time an output is
produced, it no longer accurately reflects the state of the world. This poses a
challenge, as robotics applications require minimal reaction times, such that
maneuvers can be quickly planned in the event of a safety-critical situation.
In this paper we propose StrObe, a novel approach that minimizes latency by
ingesting LiDAR packets and emitting a stream of detections without waiting for
the full sweep to be built. StrObe reuses computations from previous packets
and iteratively updates a latent spatial representation of the scene, which
acts as a memory, as new evidence comes in, resulting in accurate low-latency
perception. We demonstrate the effectiveness of our approach on a large scale
real-world dataset, showing that StrObe far outperforms the state-of-the-art
when latency is taken into account, and matches the performance in the
traditional setting.
