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http://dx.doi.org/10.7746/jkros.2022.17.2.124

Real-Time Correction Based on wheel Odometry to Improve Pedestrian Tracking Performance in Small Mobile Robot  

Park, Jaehun (Hanyang University)
Ahn, Min Sung (UCLA)
Han, Jeakweon (Hanyang University)
Publication Information
The Journal of Korea Robotics Society / v.17, no.2, 2022 , pp. 124-132 More about this Journal
Abstract
With growth in intelligence of mobile robots, interaction with humans is emerging as a very important issue for mobile robots and the pedestrian tracking technique following the designated person is adopted in many cases in a way that interacts with humans. Among the existing multi-object tracking techniques for pedestrian tracking, Simple Online and Realtime Tracking (SORT) is suitable for small mobile robots that require real-time processing while having limited computational performance. However, SORT fails to reflect changes in object detection values caused by the movement of the mobile robot, resulting in poor tracking performance. In order to solve this performance degradation, this paper proposes a more stable pedestrian tracking algorithm by correcting object tracking errors caused by robot movement in real time using wheel odometry information of a mobile robot and dynamically managing the survival period of the tracker that tracks the object. In addition, the experimental results show that the proposed methodology using data collected from actual mobile robots maintains real-time and has improved tracking accuracy with resistance to the movement of the mobile robot.
Keywords
Multiple Object Tracking; Mobile Robot; Realtime;
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Times Cited By KSCI : 5  (Citation Analysis)
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