  Ground segmentation of point clouds remains challenging because of the sparse
and unordered data structure. This paper proposes the GSECnet - Ground
Segmentation network for Edge Computing, an efficient ground segmentation
framework of point clouds specifically designed to be deployable on a low-power
edge computing unit. First, raw point clouds are converted into a
discretization representation by pillarization. Afterward, features of points
within pillars are fed into PointNet to get the corresponding pillars feature
map. Then, a depthwise-separable U-Net with the attention module learns the
classification from the pillars feature map with an enormously diminished model
parameter size. Our proposed framework is evaluated on SemanticKITTI against
both point-based and discretization-based state-of-the-art learning approaches,
and achieves an excellent balance between high accuracy and low computing
complexity. Remarkably, our framework achieves the inference runtime of 135.2
Hz on a desktop platform. Moreover, experiments verify that it is deployable on
a low-power edge computing unit powered 10 watts only.
