  Robots and autonomous systems need to know where they are within a map to
navigate effectively. Thus, simultaneous localization and mapping or SLAM is a
common building block of robot navigation systems. When building a map via a
SLAM system, robots need to re-recognize places to find loop closure and reduce
the odometry drift. Image-based place recognition received a lot of attention
in computer vision, and in this work, we investigate how such approaches can be
used for 3D LiDAR data. Recent LiDAR sensors produce high-resolution 3D scans
in combination with comparably stable intensity measurements. Through a
cylindrical projection, we can turn this information into a panoramic image. As
a result, we can apply techniques from visual place recognition to LiDAR
intensity data. The question of how well this approach works in practice has
not been answered so far. This paper provides an analysis of how such visual
techniques can be with LiDAR data, and we provide an evaluation on different
datasets. Our results suggest that this form of place recognition is possible
and an effective means for determining loop closures.
