LAMP: Large-Scale Autonomous Mapping and Positioning for Exploration of\n Perceptually-Degraded Subterranean Environments Article Swipe

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Kamak Ebadi , Yun Sil 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

Simultaneous Localization and Mapping (SLAM) in large-scale, unknown, and\ncomplex subterranean environments is a challenging problem. Sensors must\noperate in off-nominal conditions; uneven and slippery terrains make wheel\nodometry inaccurate, while long corridors without salient features make\nexteroceptive sensing ambiguous and prone to drift; finally, spurious loop\nclosures that are frequent in environments with repetitive appearance, such as\ntunnels and mines, could result in a significant distortion of the entire map.\nThese challenges are in stark contrast with the need to build highly-accurate\n3D maps to support a wide variety of applications, ranging from disaster\nresponse to the exploration of underground extraterrestrial worlds. This paper\nreports on the implementation and testing of a lidar-based multi-robot SLAM\nsystem developed in the context of the DARPA Subterranean Challenge. We present\na system architecture to enhance subterranean operation, including an accurate\nlidar-based front-end, and a flexible and robust back-end that automatically\nrejects outlying loop closures. We present an extensive evaluation in\nlarge-scale, challenging subterranean environments, including the results\nobtained in the Tunnel Circuit of the DARPA Subterranean Challenge. Finally, we\ndiscuss potential improvements, limitations of the state of the art, and future\nresearch directions.\n

Related Topics

Simultaneous Localization And Mapping

Artificial Intelligence

Concepts

Lidar Computer science Context (archaeology) Odometry Ranging Simultaneous localization and mapping Salient Scale (ratio) Artificial intelligence Terrain Computer vision Robot Spurious relationship Remote sensing Geology Mobile robot Cartography Machine learning Geography Telecommunications Paleontology

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2003.01744
PDF: https://arxiv.org/pdf/2003.01744
OA Status: green
Cited By: 2
Related Works: 10
OpenAlex ID: https://openalex.org/W4286783894

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DOI: https://doi.org/10.48550/arxiv.2003.01744

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Title: LAMP: Large-Scale Autonomous Mapping and Positioning for Exploration of\n Perceptually-Degraded Subterranean Environments

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Type: preprint

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Language: en

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Publication year: 2020

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Publication date: 2020-03-03

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Authors: Kamak Ebadi, Yun Sil Chang, Matteo Palieri, Alex Stephens, Alex Hatteland, Eric Heiden, Abhishek Thakur, Nobuhiro Funabiki, Benjamin Morrell, Sally L. Wood, Luca Carlone, Ali‐akbar Agha‐mohammadi

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Concepts: Lidar, Computer science, Context (archaeology), Odometry, Ranging, Simultaneous localization and mapping, Salient, Scale (ratio), Artificial intelligence, Terrain, Computer vision, Robot, Spurious relationship, Remote sensing, Geology, Mobile robot, Cartography, Machine learning, Geography, Telecommunications, Paleontology

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Cited by: 2

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