Simultaneous Localization and Mapping in the Epoch of Semantics: A Survey
Simultaneous Localization and Mapping (SLAM) with an astonishing research history of over three decades has brought the concept to the door step of truly autonomous robotic systems. The concept has advanced beyond the map building and self-localization of robot on the map. On the other hand, the lon...
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| Published in | International journal of control, automation, and systems Vol. 17; no. 3; pp. 729 - 742 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Bucheon / Seoul
Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers
01.03.2019
Springer Nature B.V 제어·로봇·시스템학회 |
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| Online Access | Get full text |
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| Abstract | Simultaneous Localization and Mapping (SLAM) with an astonishing research history of over three decades has brought the concept to the door step of truly autonomous robotic systems. The concept has advanced beyond the map building and self-localization of robot on the map. On the other hand, the long-standing challenges pertaining to the provision of out of the box solution for range of conditions still needs to be addressed. However, the technological advancements in the area is steadily making its ways into industry. This paper surveys state-of-the-art SLAM and discuss the insights of existing methods. Starting with a classical definition of SLAM, a brief conceptual overview, and formulation of a standard SLAM system is carried out. While discussing the auxiliaries for solving SLAM, the influx of machine learning into SLAM is also addressed. Moreover, recent SLAM algorithms have been reviewed with a focus on emerging concept of semantics to augment the system. In this paper a taxonomy of recently developed SLAM algorithms with a detailed comparison metrics, is presented. Furthermore, open challenges, future directions and emerging research issues have also been laid down. |
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| AbstractList | Simultaneous Localization and Mapping (SLAM) with an astonishing research history of over three decades has brought the concept to the door step of truly autonomous robotic systems. The concept has advanced beyond the map building and self–localization of robot on the map. On the other hand, the long–standing challenges pertaining to the provision of out of the box solution for range of conditions still needs to be addressed. However, the technological advancements in the area is steadily making its ways into industry. This paper surveys state–of–the–art SLAM and discuss the insights of existing methods. Starting with a classical definition of SLAM, a brief conceptual overview, and formulation of a standard SLAM system is carried out. While discussing the auxiliaries for solving SLAM, the influx of machine learning into SLAM is also addressed. Moreover, recent SLAM algorithms have been reviewed with a focus on emerging concept of semantics to augment the system. In this paper a taxonomy of recently developed SLAM algorithms with a detailed comparison metrics, is presented. Furthermore, open challenges, future directions and emerging research issues have also been laid down. Simultaneous Localization and Mapping (SLAM) with an astonishing research history of over threedecades has brought the concept to the door step of truly autonomous robotic systems. The concept has advancedbeyond the map building and self-localization of robot on the map. On the other hand, the long-standing challengespertaining to the provision of out of the box solution for range of conditions still needs to be addressed. However, thetechnological advancements in the area is steadily making its ways into industry. This paper surveys state-of-the-artSLAM and discuss the insights of existing methods. Starting with a classical definition of SLAM, a brief conceptualoverview, and formulation of a standard SLAM system is carried out. While discussing the auxiliaries for solvingSLAM, the influx of machine learning into SLAM is also addressed. Moreover, recent SLAM algorithms havebeen reviewed with a focus on emerging concept of semantics to augment the system. In this paper a taxonomyof recently developed SLAM algorithms with a detailed comparison metrics, is presented. Furthermore, openchallenges, future directions and emerging research issues have also been laid down. KCI Citation Count: 30 |
| Author | Sualeh, Muhammad Kim, Gon-Woo |
| Author_xml | – sequence: 1 givenname: Muhammad surname: Sualeh fullname: Sualeh, Muhammad organization: Intelligent Robotics Laboratory, Control and Robotics Engineering Department, Chungbuk National University – sequence: 2 givenname: Gon-Woo surname: Kim fullname: Kim, Gon-Woo email: gwkim@cbnu.ac.kr organization: Intelligent Robotics Laboratory, Control and Robotics Engineering Department, Chungbuk National University |
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| Keywords | Bundle adjustment deep learning SLAM semantics pose graph optimization |
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| Snippet | Simultaneous Localization and Mapping (SLAM) with an astonishing research history of over three decades has brought the concept to the door step of truly... Simultaneous Localization and Mapping (SLAM) with an astonishing research history of over threedecades has brought the concept to the door step of truly... |
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| SubjectTerms | Algorithms Control Control theory Engineering Localization Machine learning Mechatronics Robotics Semantics Simultaneous localization and mapping Taxonomy 제어계측공학 |
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| Title | Simultaneous Localization and Mapping in the Epoch of Semantics: A Survey |
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