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Abstract:   (19 Views)
Even when simultaneous localization and mapping (SLAM) solutions have been broadly developed, the vast majority of them relate to a single robot performing measurements in static environments. Researches show that the performance of SLAM algorithms deteriorates under dynamic environments. In this paper, a multi-robot simultaneous localization and mapping (MR-SLAM) system is implemented within a dynamic environment. A probabilistic approach based on extended Kalman filter (EKF) is proposed to detect moving landmarks and consequently improve the performance of SLAM in dynamic environments. The expected landmark area (ELA) is introduced. This concept allows identifying and filtering the moving landmarks. Several experiments are performed varying the speed and number of moving landmarks within the environment to investigate the effect of dynamism level and landmark speed on. The root mean square error (RMSE) is used as a form of measuring the performance of the algorithm. Results show moving landmarks, degrade the performance of classical EKF-SLAM. However, the proposed method is robust to environmental changes and is less affected by the increasing speed of the moving landmarks.
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  • Introducing multi-robot simultaneous localization and mapping (MR-SLAM) system is implemented within a dynamic environment.
  • Using a probabilistic approach based on EKF is proposed to detect moving landmarks and consequently improve the performance of SLAM in dynamic environments.
  • Introducing the expected landmark area (ELA).

Type of Study: Research Paper | Subject: Multi-Agent Systems
Received: 2019/12/05 | Accepted: 2020/01/31 | Published: 2020/03/26

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© 2020 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.