  Light detection and ranging (LiDAR) sensors are becoming available on modern
mobile devices and provide a 3D sensing capability. This new capability is
beneficial for perceptions in various use cases, but it is challenging for
resource-constrained mobile devices to use the perceptions in real-time because
of their high computational complexity. In this context, edge computing can be
used to enable LiDAR online perceptions, but offloading the perceptions on the
edge server requires a low-latency, lightweight, and efficient compression due
to the large volume of LiDAR point clouds data.
  This paper presents FLiCR, a fast and lightweight LiDAR point cloud
compression method for enabling edge-assisted online perceptions. FLiCR is
based on range images (RI) as an intermediate representation (IR), and
dictionary coding for compressing RIs. FLiCR achieves its benefits by
leveraging lossy RIs, and we show the efficiency of bytestream compression is
largely improved with quantization and subsampling. In addition, we identify
the limitation of current quality metrics for presenting the entropy of a point
cloud, and introduce a new metric that reflects both point-wise and
entropy-wise qualities for lossy IRs. The evaluation results show FLiCR is more
suitable for edge-assisted real-time perceptions than the existing LiDAR
compressions, and we demonstrate the effectiveness of our compression and
metric with the evaluations on 3D object detection and LiDAR SLAM.
