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A Socially Aware SLAM Technique Augmented by Person Tracking Module

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Abstract

In recent years the development of Simultaneous Localization and Mapping (SLAM) techniques have enabled social robots to autonomously navigate in routine human workplaces. However, most common SLAM techniques are developed for mapping and localization in static worlds. In this paper, we have developed and analyzed a novel augmentation of the FastSLAM algorithm, including a person tracking module using 2D LiDAR sensor data. Utilizing this module, the SLAM algorithm is capable of filtering measurements coming from walking people who produce noises due to their intrinsic dynamic and unstableness. This augmentation was developed and then tested on our socially assistive mobile robot platform, Arash, while moving in populated environments utilizing Robotic Operating System (ROS) as a middleware. This new approach demonstrated a clearer representation of the mapped environment and therefore more accurate localization and navigation compared to its static SLAM predecessor.

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Acknowledgments

This project was supported and funded by the Iranian National Science Foundation-INSF (http://en.insf.org/) and the Office of the Vice-President in Science and Technology.

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Authors and Affiliations

  1. Social & Cognitive Robotics Laboratory, Center of Excellence in Design, Robotics, and Automation (CEDRA), Sharif University of Technology, Tehran, Iran

    E. Ahmadi, A. Meghdari & M. Alemi

  2. Department of Humanities, Islamic Azad University, West-Tehran Branch, Tehran, Iran

    M. Alemi

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  1. E. Ahmadi
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  2. A. Meghdari
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  3. M. Alemi
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Correspondence to A. Meghdari.

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Ahmadi, E., Meghdari, A. & Alemi, M. A Socially Aware SLAM Technique Augmented by Person Tracking Module. J Intell Robot Syst 99, 3–12 (2020). https://doi.org/10.1007/s10846-019-01120-z

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  • DOI: https://doi.org/10.1007/s10846-019-01120-z

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