A Comprehensive Survey of Visual SLAM Algorithms

Barros, A.; Michel, Maugan; Moline, Yoann; Corre, Gwenolé; Carrel, F.

doi:10.3390/robotics11010024

Cited by 160 publications

(68 citation statements)

References 108 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Each of these papers reviews the major advantages and disadvantages of employing VSLAM approaches. Macario Barros et al [ 17 ] divided approaches into three different classes: visual-only (monocular), visual-inertial (stereo), and RGB-D. They also proposed various criteria for simplifying and analyzing VSLAM algorithms.…”

Section: Related Surveysmentioning

confidence: 99%

Visual SLAM: What Are the Current Trends and What to Expect?

Tourani

Bavle

Sánchez-López

et al. 2022

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: Related Surveysmentioning

confidence: 99%

Visual SLAM: What Are the Current Trends and What to Expect?

Tourani

Bavle

Sánchez-López

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Although, early solutions of SLAM had utilized traditional robotics sensor elements, such as IMU, and versatile distance and waypoint detectors, with more recent advances in the field of visual informatics and machine vision, another subgenre -often referred to as visual SLAM or vSLAM -has been established [7]. Modern [20][21][22], and RGB-Depth SLAM or RGB-D SLAM [23][24][25].…”

Section: Simultaneous Localization and Mappingmentioning

confidence: 99%

“…A crowdsourcing-based mapping solution, such as CrowdMapping [5], whose purpose is to offer low latency and accurate map updates for autonomous vehicles, can be supported and aided using robust SLAM technologies. One well-researched subfield of SLAM is visual SLAM, or vSLAM [6][7][8] for short, wherein localization, mapping and sometimes path planning is achieved using visual information. That information may be supplied in the form of strictly 2D images -e.g., passive monocular camera RGB image streams or stereo camera systems -or 3D image information provided by specific sensors -e.g., light detection and ranging (LiDAR) sensors or active RGB + Depth (RGB-D) cameras.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

ATDN vSLAM: An All-Through Deep Learning-Based Solution for Visual Simultaneous Localization and Mapping

Szántó

Bogár

Vajta

2022

Period. Polytech. Elec. Eng. Comp. Sci.

View full text Add to dashboard Cite

In this paper, a novel solution is introduced for visual Simultaneous Localization and Mapping (vSLAM) that is built up of Deep Learning components. The proposed architecture is a highly modular framework in which each component offers state of the art results in their respective fields of vision-based Deep Learning solutions. The paper shows that with the synergic integration of these individual building blocks, a functioning and efficient all-through deep neural (ATDN) vSLAM system can be created. The Embedding Distance Loss function is introduced and using it the ATDN architecture is trained. The resulting system managed to achieve 4.4% translation and 0.0176 deg/m rotational error on a subset of the KITTI dataset. The proposed architecture can be used for efficient and low-latency autonomous driving (AD) aiding database creation as well as a basis for autonomous vehicle (AV) control.

show abstract

“…Ref. [21] introduces in detail the SLAM method based on monocular, binocular, RGB-D, and visual-inertial fusion, and gives the existing problems and future direction. Ref.…”

Section: Introductionmentioning

confidence: 99%

An Overview on Visual SLAM: From Tradition to Semantic

Chen

Shang

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

Visual SLAM (VSLAM) has been developing rapidly due to its advantages of low-cost sensors, the easy fusion of other sensors, and richer environmental information. Traditional visionbased SLAM research has made many achievements, but it may fail to achieve wished results in challenging environments. Deep learning has promoted the development of computer vision, and the combination of deep learning and SLAM has attracted more and more attention. Semantic information, as high-level environmental information, can enable robots to better understand the surrounding environment. This paper introduces the development of VSLAM technology from two aspects: traditional VSLAM and semantic VSLAM combined with deep learning. For traditional VSLAM, we summarize the advantages and disadvantages of indirect and direct methods in detail and give some classical VSLAM open-source algorithms. In addition, we focus on the development of semantic VSLAM based on deep learning. Starting with typical neural networks CNN and RNN, we summarize the improvement of neural networks for the VSLAM system in detail. Later, we focus on the help of target detection and semantic segmentation for VSLAM semantic information introduction. We believe that the development of the future intelligent era cannot be without the help of semantic technology. Introducing deep learning into the VSLAM system to provide semantic information can help robots better perceive the surrounding environment and provide people with higher-level help.

show abstract

A Comprehensive Survey of Visual SLAM Algorithms

Cited by 160 publications

References 108 publications

Visual SLAM: What Are the Current Trends and What to Expect?

Visual SLAM: What Are the Current Trends and What to Expect?

ATDN vSLAM: An All-Through Deep Learning-Based Solution for Visual Simultaneous Localization and Mapping

An Overview on Visual SLAM: From Tradition to Semantic

Contact Info

Product

Resources

About