  Light Detection and Ranging (LiDAR) technology has proven to be an important
part of many robotics systems. Surface normals estimated from LiDAR data are
commonly used for a variety of tasks in such systems. As most of the today's
mechanical LiDAR sensors produce sparse data, estimating normals from a single
scan in a robust manner poses difficulties.
  In this paper, we address the problem of estimating normals for sparse LiDAR
data avoiding the typical issues of smoothing out the normals in high curvature
areas.
  Mechanical LiDARs rotate a set of rigidly mounted lasers. One firing of such
a set of lasers produces an array of points where each point's neighbor is
known due to the known firing pattern of the scanner. We use this knowledge to
connect these points to their neighbors and label them using the angles of the
lines connecting them. When estimating normals at these points, we only
consider points with the same label as neighbors. This allows us to avoid
estimating normals in high curvature areas.
  We evaluate our approach on various data, both self-recorded and publicly
available, acquired using various sparse LiDAR sensors. We show that using our
method for normal estimation leads to normals that are more robust in areas
with high curvature which leads to maps of higher quality. We also show that
our method only incurs a constant factor runtime overhead with respect to a
lightweight baseline normal estimation procedure and is therefore suited for
operation in computationally demanding environments.
