We introduce a LiDAR inertial odometry (LIO) framework, called LiPO, that enables
direct comparisons of different iterative closest point (ICP) point cloud
registration methods. The two common ICP methods we compare are point-to-point
(P2P) and point-to-feature (P2F). In our experience, within the context of LIO,
P2F-ICP results in less drift and improved mapping accuracy when robots move
aggressively through challenging environments when compared to P2P-ICP. However,
P2F-ICP methods require more hand-tuned hyper-parameters that make P2F-ICP less
general across all environments and motions. In real-world field robotics
applications where robots are used across different environments, more general
P2P-ICP methods may be preferred despite increased drift. In this paper, we seek
to better quantify the trade-off between P2P-ICP and P2F-ICP to help inform when
each method should be used. To explore this trade-off, we use LiPO to directly
compare ICP methods and test on relevant benchmark datasets as well as on our
custom unpiloted ground vehicle (UGV). We find that overall, P2F-ICP has reduced
drift and improved mapping accuracy, but, P2P-ICP is more consistent across all
environments and motions with minimal drift increase.