LiPO: LiDAR Inertial Odometry for ICP Comparison

TL;DR

This paper introduces LiPO, a framework for comparing ICP methods in LiDAR inertial odometry, revealing trade-offs between point-to-point and point-to-feature approaches in terms of drift and accuracy.

Contribution

LiPO enables direct comparison of ICP methods in LIO, providing insights into their performance trade-offs across different environments and motions.

Findings

P2F-ICP reduces drift and improves accuracy in challenging environments.

P2P-ICP offers more consistency across diverse environments.

P2F-ICP requires more hyper-parameter tuning.

Abstract

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.