Direct LiDAR Odometry: Fast Localization with Dense Point Clouds

TL;DR

This paper introduces a lightweight, real-time LiDAR odometry method that efficiently processes dense point clouds for accurate localization on computationally-limited robots, outperforming existing solutions in challenging environments.

Contribution

The paper presents a novel, efficient LiDAR odometry algorithm with a keyframing system and custom ICP solver, enabling fast, accurate localization with minimal preprocessing.

Findings

More accurate than current state-of-the-art methods

Lower computational overhead

Validated in challenging environments on aerial and legged robots

Abstract

Field robotics in perceptually-challenging environments require fast and accurate state estimation, but modern LiDAR sensors quickly overwhelm current odometry algorithms. To this end, this paper presents a lightweight frontend LiDAR odometry solution with consistent and accurate localization for computationally-limited robotic platforms. Our Direct LiDAR Odometry (DLO) method includes several key algorithmic innovations which prioritize computational efficiency and enables the use of dense, minimally-preprocessed point clouds to provide accurate pose estimates in real-time. This is achieved through a novel keyframing system which efficiently manages historical map information, in addition to a custom iterative closest point solver for fast point cloud registration with data structure recycling. Our method is more accurate with lower computational overhead than the current state-of-the-art and has been extensively evaluated in multiple perceptually-challenging environments on aerial and legged robots as part of NASA JPL Team CoSTAR's research and development efforts for the DARPA Subterranean Challenge.