A Comprehensive Evaluation of LiDAR Odometry Techniques

TL;DR

This paper provides a comprehensive empirical evaluation of LiDAR odometry techniques, analyzing various pipeline components across diverse datasets to recommend optimal design choices for accuracy and reliability.

Contribution

It systematically compares LO pipeline components through extensive ablation studies, offering data-driven guidelines for future LiDAR odometry system design.

Findings

Certain pipeline components significantly improve accuracy.

Performance varies notably across different environments and LiDAR types.

Recommendations for robust and precise LO pipeline configurations.

Abstract

Light Detection and Ranging (LiDAR) sensors have become the sensor of choice for many robotic state estimation tasks. Because of this, in recent years there has been significant work done to fine the most accurate method to perform state estimation using these sensors. In each of these prior works, an explosion of possible technique combinations has occurred, with each work comparing LiDAR Odometry (LO) "pipelines" to prior "pipelines". Unfortunately, little work up to this point has performed the significant amount of ablation studies comparing the various building-blocks of a LO pipeline. In this work, we summarize the various techniques that go into defining a LO pipeline and empirically evaluate these LO components on an expansive number of datasets across environments, LiDAR types, and vehicle motions. Finally, we make empirically-backed recommendations for the design of future LO pipelines to provide the most accurate and reliable performance.