Direct LiDAR Odometry: Fast Localization with Dense Point Clouds
This work addresses localization for computationally-limited robotic platforms in perceptually-challenging environments, representing an incremental improvement with specific algorithmic innovations.
The paper tackles the problem of fast and accurate state estimation for robots in challenging environments by introducing Direct LiDAR Odometry (DLO), a lightweight method that uses dense point clouds to provide real-time pose estimates with lower computational overhead than state-of-the-art approaches.
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.