  In this paper, we describe a strategy for training neural networks for object
detection in range images obtained from one type of LiDAR sensor using labeled
data from a different type of LiDAR sensor. Additionally, an efficient model
for object detection in range images for use in self-driving cars is presented.
Currently, the highest performing algorithms for object detection from LiDAR
measurements are based on neural networks. Training these networks using
supervised learning requires large annotated datasets. Therefore, most research
using neural networks for object detection from LiDAR point clouds is conducted
on a very small number of publicly available datasets. Consequently, only a
small number of sensor types are used. We use an existing annotated dataset to
train a neural network that can be used with a LiDAR sensor that has a lower
resolution than the one used for recording the annotated dataset. This is done
by simulating data from the lower resolution LiDAR sensor based on the higher
resolution dataset. Furthermore, improvements to models that use LiDAR range
images for object detection are presented. The results are validated using both
simulated sensor data and data from an actual lower resolution sensor mounted
to a research vehicle. It is shown that the model can detect objects from
360{\deg} range images in real time.
