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JRM Vol.33 No.1 pp. 33-43
doi: 10.20965/jrm.2021.p0033
(2021)

Paper:

Development and Experimental Verification of a Person Tracking System of Mobile Robots Using Sensor Fusion of Inertial Measurement Unit and Laser Range Finder for Occlusion Avoidance

Kazuhiro Funato*, Ryosuke Tasaki**, Hiroto Sakurai*, and Kazuhiko Terashima*

*Toyohashi University of Technology
1-1 Hibarigaoka, Tempaku-cho, Toyohashi, Aichi 441-8580, Japan

**Aoyama Gakuin University
5-10-1 Fuchinobe, Chuo-ku, Sagamihara, Kanagawa 252-5258, Japan

Received:
December 1, 2019
Accepted:
December 24, 2020
Published:
February 20, 2021
Keywords:
person tracking system of mobile robot, inertial sensor, sensor fusion, occlusion
Abstract
Development and Experimental Verification of a Person Tracking System of Mobile Robots Using Sensor Fusion of Inertial Measurement Unit and Laser Range Finder for Occlusion Avoidance

A case of misidentifying a passerby as a tracking target

The authors have been developing a mobile robot to assist doctors in hospitals in managing medical tools and patient electronic medical records. The robot tracks behind a mobile medical worker while maintaining a constant distance from the worker. However, it was difficult to detect objects in the sensor’s invisible region, called occlusion. In this study, we propose a sensor fusion method to estimate the position of a robot tracking target indirectly by an inertial measurement unit (IMU) in addition to the direct measurement by an laser range finder (LRF) and develop a human tracking system to avoid occlusion by a mobile robot. Based on this, we perform detailed experimental verification of tracking a specified person to verify the validity of the proposed method.

Cite this article as:
Kazuhiro Funato, Ryosuke Tasaki, Hiroto Sakurai, and Kazuhiko Terashima, “Development and Experimental Verification of a Person Tracking System of Mobile Robots Using Sensor Fusion of Inertial Measurement Unit and Laser Range Finder for Occlusion Avoidance,” J. Robot. Mechatron., Vol.33, No.1, pp. 33-43, 2021.
Data files:
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Last updated on Mar. 05, 2021