LOCUS 2.0: Robust and Computationally Efficient Lidar Odometry for Real-Time 3D Mapping Article Swipe
Andrzej Reinke
,
Matteo Palieri
,
Benjamin Morrell
,
Yun Chang
,
Kamak Ebadi
,
Luca Carlone
,
Ali–akbar Agha–mohammadi
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/lra.2022.3181357
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/lra.2022.3181357
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 \morrell{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 $11~h$ of operations and $16~km$ of distance traveled.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/lra.2022.3181357
- OA Status
- green
- Cited By
- 86
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4281555123
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4281555123Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/lra.2022.3181357Digital Object Identifier
- Title
-
LOCUS 2.0: Robust and Computationally Efficient Lidar Odometry for Real-Time 3D MappingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-06-10Full publication date if available
- Authors
-
Andrzej Reinke, Matteo Palieri, Benjamin Morrell, Yun Chang, Kamak Ebadi, Luca Carlone, Ali–akbar Agha–mohammadiList of authors in order
- Landing page
-
https://doi.org/10.1109/lra.2022.3181357Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2205.11784Direct OA link when available
- Concepts
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Odometry, Computer science, Computation, Point cloud, Lidar, Computer vision, Artificial intelligence, Robot, Real-time computing, Mobile robot, Remote sensing, Algorithm, GeographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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86Total citation count in OpenAlex
- Citations by year (recent)
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2025: 16, 2024: 38, 2023: 29, 2022: 3Per-year citation counts (last 5 years)
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23Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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