SLAM in Dynamic Environments: A Deep Learning Approach for Moving Object Tracking Using ML-RANSAC Algorithm
The important problem of Simultaneous Localization and Mapping (SLAM) in dynamic environments is less studied than the counterpart problem in static settings. In this paper, we present a solution for the feature-based SLAM problem in dynamic environments. We propose an algorithm that integrates SLAM...
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| Published in | Sensors (Basel, Switzerland) Vol. 19; no. 17; p. 3699 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
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MDPI AG
26.08.2019
MDPI |
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| Online Access | Get full text |
| ISSN | 1424-8220 1424-8220 |
| DOI | 10.3390/s19173699 |
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| Abstract | The important problem of Simultaneous Localization and Mapping (SLAM) in dynamic environments is less studied than the counterpart problem in static settings. In this paper, we present a solution for the feature-based SLAM problem in dynamic environments. We propose an algorithm that integrates SLAM with multi-target tracking (SLAMMTT) using a robust feature-tracking algorithm for dynamic environments. A novel implementation of RANdomSAmple Consensus (RANSAC) method referred to as multilevel-RANSAC (ML-RANSAC) within the Extended Kalman Filter (EKF) framework is applied for multi-target tracking (MTT). We also apply machine learning to detect features from the input data and to distinguish moving from stationary objects. The data stream from LIDAR and vision sensors are fused in real-time to detect objects and depth information. A practical experiment is designed to verify the performance of the algorithm in a dynamic environment. The unique feature of this algorithm is its ability to maintain tracking of features even when the observations are intermittent whereby many reported algorithms fail in such situations. Experimental validation indicates that the algorithm is able to perform consistent estimates in a fast and robust manner suggesting its feasibility for real-time applications. |
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| AbstractList | The important problem of Simultaneous Localization and Mapping (SLAM) in dynamic environments is less studied than the counterpart problem in static settings. In this paper, we present a solution for the feature-based SLAM problem in dynamic environments. We propose an algorithm that integrates SLAM with multi-target tracking (SLAMMTT) using a robust feature-tracking algorithm for dynamic environments. A novel implementation of RANdomSAmple Consensus (RANSAC) method referred to as multilevel-RANSAC (ML-RANSAC) within the Extended Kalman Filter (EKF) framework is applied for multi-target tracking (MTT). We also apply machine learning to detect features from the input data and to distinguish moving from stationary objects. The data stream from LIDAR and vision sensors are fused in real-time to detect objects and depth information. A practical experiment is designed to verify the performance of the algorithm in a dynamic environment. The unique feature of this algorithm is its ability to maintain tracking of features even when the observations are intermittent whereby many reported algorithms fail in such situations. Experimental validation indicates that the algorithm is able to perform consistent estimates in a fast and robust manner suggesting its feasibility for real-time applications. The important problem of Simultaneous Localization and Mapping (SLAM) in dynamic environments is less studied than the counterpart problem in static settings. In this paper, we present a solution for the feature-based SLAM problem in dynamic environments. We propose an algorithm that integrates SLAM with multi-target tracking (SLAMMTT) using a robust feature-tracking algorithm for dynamic environments. A novel implementation of RANdomSAmple Consensus (RANSAC) method referred to as multilevel-RANSAC (ML-RANSAC) within the Extended Kalman Filter (EKF) framework is applied for multi-target tracking (MTT). We also apply machine learning to detect features from the input data and to distinguish moving from stationary objects. The data stream from LIDAR and vision sensors are fused in real-time to detect objects and depth information. A practical experiment is designed to verify the performance of the algorithm in a dynamic environment. The unique feature of this algorithm is its ability to maintain tracking of features even when the observations are intermittent whereby many reported algorithms fail in such situations. Experimental validation indicates that the algorithm is able to perform consistent estimates in a fast and robust manner suggesting its feasibility for real-time applications.The important problem of Simultaneous Localization and Mapping (SLAM) in dynamic environments is less studied than the counterpart problem in static settings. In this paper, we present a solution for the feature-based SLAM problem in dynamic environments. We propose an algorithm that integrates SLAM with multi-target tracking (SLAMMTT) using a robust feature-tracking algorithm for dynamic environments. A novel implementation of RANdomSAmple Consensus (RANSAC) method referred to as multilevel-RANSAC (ML-RANSAC) within the Extended Kalman Filter (EKF) framework is applied for multi-target tracking (MTT). We also apply machine learning to detect features from the input data and to distinguish moving from stationary objects. The data stream from LIDAR and vision sensors are fused in real-time to detect objects and depth information. A practical experiment is designed to verify the performance of the algorithm in a dynamic environment. The unique feature of this algorithm is its ability to maintain tracking of features even when the observations are intermittent whereby many reported algorithms fail in such situations. Experimental validation indicates that the algorithm is able to perform consistent estimates in a fast and robust manner suggesting its feasibility for real-time applications. |
| Author | Bahraini, Masoud S. Bozorg, Mohammad Rad, Ahmad B. |
| AuthorAffiliation | 1 Department of Mechanical Engineering, Sirjan University of Technology, Sirjan 78137-33385, Iran 2 School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC V3T 0A3, Canada 3 Faculty of Mechanical Engineering, Yazd University, Yazd 89195-741, Iran |
| AuthorAffiliation_xml | – name: 1 Department of Mechanical Engineering, Sirjan University of Technology, Sirjan 78137-33385, Iran – name: 2 School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC V3T 0A3, Canada – name: 3 Faculty of Mechanical Engineering, Yazd University, Yazd 89195-741, Iran |
| Author_xml | – sequence: 1 givenname: Masoud S. orcidid: 0000-0002-3692-2168 surname: Bahraini fullname: Bahraini, Masoud S. – sequence: 2 givenname: Ahmad B. surname: Rad fullname: Rad, Ahmad B. – sequence: 3 givenname: Mohammad surname: Bozorg fullname: Bozorg, Mohammad |
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| Cites_doi | 10.1002/rob.20312 10.1016/j.mechatronics.2017.12.002 10.1049/iet-spr.2015.0389 10.1109/IVS.2008.4621259 10.5772/33583 10.1109/CVPR.2014.81 10.1007/s40997-019-00294-z 10.1007/s10462-012-9365-8 10.1109/TASE.2015.2426203 10.1109/ICRA.2015.7139256 10.1109/IVS.2018.8500454 10.1016/j.neucom.2018.01.092 10.1134/S1054661816010065 10.1016/j.inffus.2010.01.004 10.1109/IVS.2011.5940576 10.3390/s18072046 10.1007/978-3-319-10584-0_23 10.1177/0278364907081229 10.5244/C.29.32 10.1109/ACC.2014.6859273 10.1007/s10514-005-0606-4 10.1002/rob.21620 10.1109/TRO.2016.2624754 10.1007/978-3-540-88688-4_37 10.1109/70.938382 10.1109/AHS.2018.8541483 10.1109/JIOT.2019.2902141 10.1002/rob.21430 10.1109/IVS.2014.6856558 |
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| Copyright | 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2019 by the authors. 2019 |
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| Keywords | RANSAC R-CNN deep learning SLAM DATMO multi-target tracking autonomous robot |
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| SubjectTerms | Algorithms autonomous robot Classification DATMO Deep learning Lasers Localization Mechanical engineering Methods multi-target tracking Neural networks Product development R-CNN RANSAC Robots Semantics Sensors SLAM |
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| Title | SLAM in Dynamic Environments: A Deep Learning Approach for Moving Object Tracking Using ML-RANSAC Algorithm |
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