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Deep Reinforcement Learning Based Loop Closure Detection

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Abstract

In this work, we investigate loop closure detection through a deep reinforcement learning approach. The loop closure detection problem correctly identifies a location or area a robot has previously visited. We propose a reward-driven optimization process that strives to learn loop closure detection. We demonstrate the framework in a simulated grid environment that generates observation data for a learning agent. We designed a grid-based environment to simulate indoor environments and train a policy for loop closure detection. A conversion of real-world map and features to the simulated environment is also demonstrated. The learning agent was tested in simulation and indoor lab environments. Our experimental results show that our algorithm can perform loop closure detection effectively.

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Acknowledgements

This research has been partially funded by the Advanced Driver Assistance System (ADAS) group at Texas Instruments (TI) in Dallas, TX, and by the University of Texas at Arlington Research Institute.

Funding

This work was supported by the University of Texas at Arlington Research Institute and the Advanced Driver Assistance System (ADAS) group at Texas Instruments (TI) in Dallas, TX.

Author information

Authors and Affiliations

  1. University of Texas at Arlington Research Institute, Fort Worth, TX, USA

    Asif Iqbal & Nicholas R. Gans

  2. Department of Computer Science and Engineering, Fort Worth, TX, USA

    Rhitu Thapa

Authors
  1. Asif Iqbal
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  2. Rhitu Thapa
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  3. Nicholas R. Gans
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Asif Iqbal. The lab experiments and data capture were accomplished by Rhitu Thapa and Asif Iqbal. The first draft of the manuscript was written by Asif Iqbal, and both authors edited and revised the manuscript. Management of the study and manuscript preparation was performed by Nicholas Gans.

Corresponding author

Correspondence to Asif Iqbal.

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There are no human or animal subjects in this study. No ethical approval is required.

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There are no human subjects in this study. There is no necessary consent to publish.

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There are no human subjects in this study. There is no necessary consent to publish.

Competing interests

The authors have no relevant financial or non-financial interests to disclose.

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Material and/or Code availability

Data sharing is not applicable to this article as no datasets were generated or analysed during the current study. Data for this research consists of simulation data and video captured by a moving robot. Video data is not publicly available, but will be retained by the authors and be made available upon reasonable request. Software written by the authors will not be publicly shared.

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This work was supported by the University of Texas at Arlington Research Institute and the Advanced Driver Assistance System (ADAS) group at Texas Instruments (TI) in Dallas, TX

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Iqbal, A., Thapa, R. & Gans, N.R. Deep Reinforcement Learning Based Loop Closure Detection. J Intell Robot Syst 106, 51 (2022). https://doi.org/10.1007/s10846-022-01720-2

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