LAMP: Large-Scale Autonomous Mapping and Positioning for Exploration of Perceptually-Degraded Subterranean Environments Article Swipe
Kamak Ebadi
,
Yun Chang
,
Matteo Palieri
,
Alex Stephens
,
Alex Hatteland
,
Eric Heiden
,
Abhishek Thakur
,
Nobuhiro Funabiki
,
Benjamin Morrell
,
Sally L. Wood
,
Luca Carlone
,
Ali‐akbar Agha‐mohammadi
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2003.01744
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2003.01744
Simultaneous Localization and Mapping (SLAM) in large-scale, unknown, and complex subterranean environments is a challenging problem. Sensors must operate in off-nominal conditions; uneven and slippery terrains make wheel odometry inaccurate, while long corridors without salient features make exteroceptive sensing ambiguous and prone to drift; finally, spurious loop closures that are frequent in environments with repetitive appearance, such as tunnels and mines, could result in a significant distortion of the entire map. These challenges are in stark contrast with the need to build highly-accurate 3D maps to support a wide variety of applications, ranging from disaster response to the exploration of underground extraterrestrial worlds. This paper reports on the implementation and testing of a lidar-based multi-robot SLAM system developed in the context of the DARPA Subterranean Challenge. We present a system architecture to enhance subterranean operation, including an accurate lidar-based front-end, and a flexible and robust back-end that automatically rejects outlying loop closures. We present an extensive evaluation in large-scale, challenging subterranean environments, including the results obtained in the Tunnel Circuit of the DARPA Subterranean Challenge. Finally, we discuss potential improvements, limitations of the state of the art, and future research directions.
Related Topics
Concepts
Lidar
Computer science
Ranging
Context (archaeology)
Odometry
Artificial intelligence
Simultaneous localization and mapping
Scale (ratio)
Terrain
Salient
Computer vision
Robot
Remote sensing
Geology
Mobile robot
Geography
Cartography
Paleontology
Telecommunications
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2003.01744
- https://arxiv.org/pdf/2003.01744
- OA Status
- green
- Cited By
- 2
- References
- 33
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3010178856
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3010178856Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2003.01744Digital Object Identifier
- Title
-
LAMP: Large-Scale Autonomous Mapping and Positioning for Exploration of Perceptually-Degraded Subterranean EnvironmentsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-03-03Full publication date if available
- Authors
-
Kamak Ebadi, Yun Chang, Matteo Palieri, Alex Stephens, Alex Hatteland, Eric Heiden, Abhishek Thakur, Nobuhiro Funabiki, Benjamin Morrell, Sally L. Wood, Luca Carlone, Ali‐akbar Agha‐mohammadiList of authors in order
- Landing page
-
https://arxiv.org/abs/2003.01744Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2003.01744Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2003.01744Direct OA link when available
- Concepts
-
Lidar, Computer science, Ranging, Context (archaeology), Odometry, Artificial intelligence, Simultaneous localization and mapping, Scale (ratio), Terrain, Salient, Computer vision, Robot, Remote sensing, Geology, Mobile robot, Geography, Cartography, Paleontology, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2021: 2Per-year citation counts (last 5 years)
- References (count)
-
33Number of works referenced by this work
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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