Direct LiDAR Odometry: Fast Localization with Dense Point Clouds

Abstract

This paper presents a light-weight frontend Li-DAR 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 full, minimally-preprocessed point clouds to provide accurate pose estimates in real-time. This work also presents several important algorithmic insights and design choices from developing on platforms with shared or otherwise limited computational resources, including 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-theart and has been extensively evaluated in several perceptuallychallenging environments on aerial and legged robots as part of NASA JPL Team CoSTAR's research and development efforts for the DARPA Subterranean Challenge.

LOCUS 2.0: Robust and Computationally Efficient Lidar Odometry for Real-Time 3D Mapping

“…A novel formula for computing the Kalman gain results in a considerable decrease of computational complexity with respect to the standard formulation, translating into decreased computation time. The work [15] presents DLIO, a lightweight loosely-coupled lidar-inertial odometry solution for efficient operation over constrained platforms. The work provides efficient derivation of local submaps for global refinement constructed by concatenating point clouds associated with historical key-frames, along with a custom iterative closest point solver for fast and lightweight point cloud registration with data structure recycling that eliminates redundant calculations.…”