Visual SLAM: What Are the Current Trends and What to Expect?
In recent years, Simultaneous Localization and Mapping (SLAM) systems have shown significant performance, accuracy, and efficiency gain. In this regard, Visual Simultaneous Localization and Mapping (VSLAM) methods refer to the SLAM approaches that employ cameras for pose estimation and map reconstru...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/23/9297 |
| _version_ | 1797462088701968384 |
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| author | Ali Tourani Hriday Bavle Jose Luis Sanchez-Lopez Holger Voos |
| author_facet | Ali Tourani Hriday Bavle Jose Luis Sanchez-Lopez Holger Voos |
| author_sort | Ali Tourani |
| collection | DOAJ |
| description | In recent years, Simultaneous Localization and Mapping (SLAM) systems have shown significant performance, accuracy, and efficiency gain. In this regard, Visual Simultaneous Localization and Mapping (VSLAM) methods refer to the SLAM approaches that employ cameras for pose estimation and map reconstruction and are preferred over Light Detection And Ranging (LiDAR)-based methods due to their lighter weight, lower acquisition costs, and richer environment representation. Hence, several VSLAM approaches have evolved using different camera types (e.g., monocular or stereo), and have been tested on various datasets (e.g., Technische Universität München (TUM) RGB-D or European Robotics Challenge (EuRoC)) and in different conditions (i.e., indoors and outdoors), and employ multiple methodologies to have a better understanding of their surroundings. The mentioned variations have made this topic popular for researchers and have resulted in various methods. In this regard, the primary intent of this paper is to assimilate the wide range of works in VSLAM and present their recent advances, along with discussing the existing challenges and trends. This survey is worthwhile to give a big picture of the current focuses in robotics and VSLAM fields based on the concentrated resolutions and objectives of the state-of-the-art. This paper provides an in-depth literature survey of fifty impactful articles published in the VSLAMs domain. The mentioned manuscripts have been classified by different characteristics, including the novelty domain, objectives, employed algorithms, and semantic level. The paper also discusses the current trends and contemporary directions of VSLAM techniques that may help researchers investigate them. |
| first_indexed | 2024-03-09T17:31:26Z |
| format | Article |
| id | doaj.art-04bc319606094730a085907f45c37fba |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T17:31:26Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-04bc319606094730a085907f45c37fba2023-11-24T12:11:50ZengMDPI AGSensors1424-82202022-11-012223929710.3390/s22239297Visual SLAM: What Are the Current Trends and What to Expect?Ali Tourani0Hriday Bavle1Jose Luis Sanchez-Lopez2Holger Voos3Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, 1855 Luxembourg, LuxembourgInterdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, 1855 Luxembourg, LuxembourgInterdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, 1855 Luxembourg, LuxembourgInterdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, 1855 Luxembourg, LuxembourgIn recent years, Simultaneous Localization and Mapping (SLAM) systems have shown significant performance, accuracy, and efficiency gain. In this regard, Visual Simultaneous Localization and Mapping (VSLAM) methods refer to the SLAM approaches that employ cameras for pose estimation and map reconstruction and are preferred over Light Detection And Ranging (LiDAR)-based methods due to their lighter weight, lower acquisition costs, and richer environment representation. Hence, several VSLAM approaches have evolved using different camera types (e.g., monocular or stereo), and have been tested on various datasets (e.g., Technische Universität München (TUM) RGB-D or European Robotics Challenge (EuRoC)) and in different conditions (i.e., indoors and outdoors), and employ multiple methodologies to have a better understanding of their surroundings. The mentioned variations have made this topic popular for researchers and have resulted in various methods. In this regard, the primary intent of this paper is to assimilate the wide range of works in VSLAM and present their recent advances, along with discussing the existing challenges and trends. This survey is worthwhile to give a big picture of the current focuses in robotics and VSLAM fields based on the concentrated resolutions and objectives of the state-of-the-art. This paper provides an in-depth literature survey of fifty impactful articles published in the VSLAMs domain. The mentioned manuscripts have been classified by different characteristics, including the novelty domain, objectives, employed algorithms, and semantic level. The paper also discusses the current trends and contemporary directions of VSLAM techniques that may help researchers investigate them.https://www.mdpi.com/1424-8220/22/23/9297visual SLAMComputer Visionrobotics |
| spellingShingle | Ali Tourani Hriday Bavle Jose Luis Sanchez-Lopez Holger Voos Visual SLAM: What Are the Current Trends and What to Expect? Sensors visual SLAM Computer Vision robotics |
| title | Visual SLAM: What Are the Current Trends and What to Expect? |
| title_full | Visual SLAM: What Are the Current Trends and What to Expect? |
| title_fullStr | Visual SLAM: What Are the Current Trends and What to Expect? |
| title_full_unstemmed | Visual SLAM: What Are the Current Trends and What to Expect? |
| title_short | Visual SLAM: What Are the Current Trends and What to Expect? |
| title_sort | visual slam what are the current trends and what to expect |
| topic | visual SLAM Computer Vision robotics |
| url | https://www.mdpi.com/1424-8220/22/23/9297 |
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