  We focus on the problem of LiDAR point cloud based loop detection (or
Finding) and closure (LDC) in a multi-agent setting. State-of-the-art (SOTA)
techniques directly generate learned embeddings of a given point cloud, require
large data transfers, and are not robust to wide variations in 6
Degrees-of-Freedom (DOF) viewpoint. Moreover, absence of strong priors in an
unstructured point cloud leads to highly inaccurate LDC. In this original
approach, we propose independent roll and pitch canonicalization of the point
clouds using a common dominant ground plane. Discretization of the
canonicalized point cloud along the axis perpendicular to the ground plane
leads to an image similar to Digital Elevation Maps (DEMs), which exposes
strong spatial priors in the scene. Our experiments show that LDC based on
learnt embeddings of such DEMs is not only data efficient but also
significantly more robust, and generalizable than the current SOTA. We report
significant performance gain in terms of Average Precision for loop detection
and absolute translation/rotation error for relative pose estimation (or loop
closure) on Kitti, GPR and Oxford Robot Car over multiple SOTA LDC methods. Our
encoder technique allows to compress the original point cloud by over 830
times. To further test the robustness of our technique we create and opensource
a custom dataset called Lidar-UrbanFly Dataset (LUF) which consists of point
clouds obtained from a LiDAR mounted on a quadrotor.
