  This work presents an extension of graph-based SLAM methods to exploit the
potential of 3D laser scans for loop detection. Every high-dimensional point
cloud is replaced by a compact global descriptor, whereby a trained detector
decides whether a loop exists. Searching for loops is performed locally in a
variable space to consider the odometry drift. Since closing a wrong loop has
fatal consequences, an extensive verification is performed before acceptance.
The proposed algorithm is implemented as an extension of the widely used
state-of-the-art library RTAB-Map, and several experiments show the
improvement: During SLAM with a mobile service robot in changing indoor and
outdoor campus environments, our approach improves RTAB-Map regarding total
number of closed loops. Especially in the presence of significant environmental
changes, which typically lead to failure, localization becomes possible by our
extension. Experiments with a car in traffic (KITTI benchmark) show the general
applicability of our approach. These results are comparable to the
state-of-the-art LiDAR method LOAM. The developed ROS package is freely
available.
