  This paper presents a novel architecture for point cloud road user detection,
which is based on a classical point cloud proposal generator approach, that
utilizes simple geometrical rules. New methods are coupled with this technique
to achieve extremely small computational requirement, and mAP that is
comparable to the state-of-the-art. The idea is to specifically exploit
geometrical rules in hopes of faster performance. The typical downsides of this
approach, e.g. global context loss, are tackled in this paper, and solutions
are presented. This approach allows real-time performance on a single core CPU,
which is not the case with end-to-end solutions presented in the
state-of-the-art. We have evaluated the performance of the method with the
public KITTI dataset, and with our own annotated dataset collected with a small
mobile robot platform. Moreover, we also present a novel ground segmentation
method, which is evaluated with the public SemanticKITTI dataset.
