  Understanding the semantic characteristics of the environment is a key
enabler for autonomous robot operation. In this paper, we propose a deep
convolutional neural network (DCNN) for the semantic segmentation of a LiDAR
scan into the classes car, pedestrian or bicyclist. This architecture is based
on dense blocks and efficiently utilizes depth separable convolutions to limit
the number of parameters while still maintaining state-of-the-art performance.
To make the predictions from the DCNN temporally consistent, we propose a Bayes
filter based method. This method uses the predictions from the neural network
to recursively estimate the current semantic state of a point in a scan. This
recursive estimation uses the knowledge gained from previous scans, thereby
making the predictions temporally consistent and robust towards isolated
erroneous predictions. We compare the performance of our proposed architecture
with other state-of-the-art neural network architectures and report substantial
improvement. For the proposed Bayes filter approach, we show results on various
sequences in the KITTI tracking benchmark.
