  Simultaneous localization and mapping (SLAM) is a fundamental capability
required by most autonomous systems. In this paper, we address the problem of
loop closing for SLAM based on 3D laser scans recorded by autonomous cars. Our
approach utilizes a deep neural network exploiting different cues generated
from LiDAR data for finding loop closures. It estimates an image overlap
generalized to range images and provides a relative yaw angle estimate between
pairs of scans. Based on such predictions, we tackle loop closure detection and
integrate our approach into an existing SLAM system to improve its mapping
results. We evaluate our approach on sequences of the KITTI odometry benchmark
and the Ford campus dataset. We show that our method can effectively detect
loop closures surpassing the detection performance of state-of-the-art methods.
To highlight the generalization capabilities of our approach, we evaluate our
model on the Ford campus dataset while using only KITTI for training. The
experiments show that the learned representation is able to provide reliable
loop closure candidates, also in unseen environments.
