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Evaluation of RGB-D SLAM in Large Indoor Environments

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Interactive Collaborative Robotics (ICR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13719))

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Abstract

Simultaneous localization and mapping (SLAM) is one of the key components of a control system that aims to ensure autonomous navigation of a mobile robot in unknown environments. In a variety of practical cases, a robot might need to travel long distances in order to accomplish its mission. This requires long-term work of SLAM methods and building large maps. Consequently, the computational burden (including high memory consumption for map storage) becomes a bottleneck. Indeed, state-of-the-art SLAM algorithms include specific techniques and optimizations to tackle this challenge; still their performance in long-term scenarios needs proper assessment. To this end, we perform an empirical evaluation of two widespread state-of-the-art RGB-D SLAM methods, suitable for long-term navigation, i.e. RTAB-Map and Voxgraph. We evaluate them in a large simulated indoor environment, consisting of corridors and halls, while varying the odometer noise for a more realistic setup. We provide both qualitative and quantitative analysis of both methods uncovering their strengths and weaknesses. We find that both methods build a high-quality map with low odometry noise but tend to fail with high odometry noise. Voxgraph has lower relative trajectory estimation error and memory consumption than RTAB-Map, while its absolute error is higher.

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Authors and Affiliations

  1. Federal Research Center “Computer Science and Control” of Russian Academy of Sciences, Vavilov Str. 44-2, 119333, Moscow, Russia

    Kirill Muravyev & Konstantin Yakovlev

Authors
  1. Kirill Muravyev
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  2. Konstantin Yakovlev
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Correspondence to Kirill Muravyev .

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Editors and Affiliations

  1. St. Petersburg Federal Research Center of the Russian Academy of Sciences, St. Petersburg, Russia

    Andrey Ronzhin

  2. V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, Moscow, Russia

    Roman Meshcheryakov

  3. Guangdong University of Petrochemical Technology (West Gate), Maoming, China

    Zhen Xiantong

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Muravyev, K., Yakovlev, K. (2022). Evaluation of RGB-D SLAM in Large Indoor Environments. In: Ronzhin, A., Meshcheryakov, R., Xiantong, Z. (eds) Interactive Collaborative Robotics. ICR 2022. Lecture Notes in Computer Science, vol 13719. Springer, Cham. https://doi.org/10.1007/978-3-031-23609-9_9

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  • DOI: https://doi.org/10.1007/978-3-031-23609-9_9

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