A Socially Aware SLAM Technique Augmented by Person Tracking Module

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 develop...

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Bibliographic Details
Published inJournal of intelligent & robotic systems Vol. 99; no. 1; pp. 3 - 12
Main Authors Ahmadi, E., Meghdari, A., Alemi, M.
Format Journal Article
LanguageEnglish
Published Dordrecht Springer Netherlands 01.07.2020
Springer
Springer Nature B.V
Subjects
Algorithms
Artificial Intelligence
Augmentation
Computational linguistics
Control
Electrical Engineering
Engineering
Language processing
Localization
Mechanical Engineering
Mechatronics
Methods
Middleware
Modules
Natural language interfaces
Optical radar
Remote sensing
Robotics
Robotics industry
Robots
Simultaneous localization and mapping
Tracking
Walking
Workplaces
Socially assistive robots
Simultaneous localization and mapping
Person tracking
Dynamic environment
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Summary: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.
ISSN:0921-0296
1573-0409
DOI:10.1007/s10846-019-01120-z