  We present a self-supervised learning approach for the semantic segmentation
of lidar frames. Our method is used to train a deep point cloud segmentation
architecture without any human annotation. The annotation process is automated
with the combination of simultaneous localization and mapping (SLAM) and
ray-tracing algorithms. By performing multiple navigation sessions in the same
environment, we are able to identify permanent structures, such as walls, and
disentangle short-term and long-term movable objects, such as people and
tables, respectively. New sessions can then be performed using a network
trained to predict these semantic labels. We demonstrate the ability of our
approach to improve itself over time, from one session to the next. With
semantically filtered point clouds, our robot can navigate through more complex
scenarios, which, when added to the training pool, help to improve our network
predictions. We provide insights into our network predictions and show that our
approach can also improve the performances of common localization techniques.
