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

Lidar odometry has attracted considerable attention as a robust localization method for autonomous robots operating in complex GNSS-denied environments. However, achieving reliable and efficient performance on heterogeneous platforms in large-scale environments remains an open challenge due to the l...

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Bibliographic Details
Published in:IEEE robotics and automation letters Vol. 7; no. 4; pp. 9043 - 9050
Main Authors: Reinke, Andrzej, Palieri, Matteo, Morrell, Benjamin, Chang, Yun, Ebadi, Kamak, Carlone, Luca, Agha-Mohammadi, Ali-Akbar
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.10.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Computational efficiency
Darkness
data sets for SLAM
Datasets
Iterative methods
Laser radar
Lidar
Localization method
Loci
Mapping
Memory management
Odometers
Platforms
Point cloud compression
Real time
Real-time systems
Robot sensing systems
robotics in under-resourced settings
Robots
Robustness (mathematics)
sensor fusion
SLAM
Three-dimensional displays
ISSN:2377-3766, 2377-3766
Online Access:Get full text
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Summary:Lidar odometry has attracted considerable attention as a robust localization method for autonomous robots operating in complex GNSS-denied environments. However, achieving reliable and efficient performance on heterogeneous platforms in large-scale environments remains an open challenge due to the limitations of onboard computation and memory resources needed for autonomous operation. In this work, we present LOCUS 2.0, a robust and computationally-efficient lidar odometry system for real-time underground 3D mapping. LOCUS 2.0 includes a novel normals-based Generalized Iterative Closest Point (GICP) formulation that reduces the computation time of point cloud alignment, an adaptive voxel grid filter that maintains the desired computation load regardless of the environment's geometry, and a sliding-window map approach that bounds the memory consumption. The proposed approach is shown to be suitable to be deployed on heterogeneous robotic platforms involved in large-scale explorations under severe computation and memory constraints. We demonstrate LOCUS 2.0, a key element of the CoSTAR team's entry in the DARPA Subterranean Challenge, across various underground scenarios. We release LOCUS 2.0 as an open-source library and also release a lidar-based odometry dataset in challenging and large-scale underground environments. The dataset features legged and wheeled platforms in multiple environments including fog, dust, darkness, and geometrically degenerate surroundings with a total of <inline-formula><tex-math notation="LaTeX">\text{11}\;\text{h}</tex-math></inline-formula> of operations and <inline-formula><tex-math notation="LaTeX">\text{16}\;\text{km}</tex-math></inline-formula> of distance traveled.
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2022.3181357