  Highly dynamic environments, with moving objects such as cars or humans, can
pose a performance challenge for LiDAR SLAM systems that assume largely static
scenes. To overcome this challenge and support the deployment of robots in real
world scenarios, we propose a complete solution for a dynamic object aware
LiDAR SLAM algorithm. This is achieved by leveraging a real-time capable neural
network that can detect dynamic objects, thus allowing our system to deal with
them explicitly. To efficiently generate the necessary training data which is
key to our approach, we present a novel end-to-end occupancy grid based
pipeline that can automatically label a wide variety of arbitrary dynamic
objects. Our solution can thus generalize to different environments without the
need for expensive manual labeling and at the same time avoids assumptions
about the presence of a predefined set of known objects in the scene. Using
this technique, we automatically label over 12000 LiDAR scans collected in an
urban environment with a large amount of pedestrians and use this data to train
a neural network, achieving an average segmentation IoU of 0.82. We show that
explicitly dealing with dynamic objects can improve the LiDAR SLAM odometry
performance by 39.6% while yielding maps which better represent the
environments. A supplementary video as well as our test data are available
online.
