Before revision:  The experiments show the capability of our approach to highly compress lidar data, maintain a meaningful distribution of the latent space, and even incorporate time-depended information .
After revision:  The experiments show the capability of our approach for 2D-lidar scans w.r.t. the state of the art. However, as we demonstrate in the experiments the impact on reinforcement learning in lidar-based navigation tasks is non-predictable when improving the latent state representation generated by an autoencoding pipeline. This is surprising and needs to be taken into account during the process of optimizing a pretrained autoencoder for reinforcement learning tasks .