  LiDAR sensors are a powerful tool for robot simultaneous localization and
mapping (SLAM) in unknown environments, but the raw point clouds they produce
are dense, computationally expensive to store, and unsuited for direct use by
downstream autonomy tasks, such as motion planning. For integration with motion
planning, it is desirable for SLAM pipelines to generate lightweight geometric
map representations. Such representations are also particularly well-suited for
man-made environments, which can often be viewed as a so-called "Manhattan
world" built on a Cartesian grid. In this work we present a 3D LiDAR SLAM
algorithm for Manhattan world environments which extracts planar features from
point clouds to achieve lightweight, real-time localization and mapping. Our
approach generates plane-based maps which occupy significantly less memory than
their point cloud equivalents, and are suited towards fast collision checking
for motion planning. By leveraging the Manhattan world assumption, we target
extraction of orthogonal planes to generate maps which are more structured and
organized than those of existing plane-based LiDAR SLAM approaches. We
demonstrate our approach in the high-fidelity AirSim simulator and in
real-world experiments with a ground rover equipped with a Velodyne LiDAR. For
both cases, we are able to generate high quality maps and trajectory estimates
at a rate matching the sensor rate of 10 Hz.
