From SLAM to Situational Awareness: Challenges and Survey

Bavle, Hriday; Sánchez-López, José Luis; Cimarelli, Claudio; Tourani, Ali; Voos, Holger

doi:10.48550/arxiv.2110.00273

Cited by 3 publications

(3 citation statements)

References 157 publications

(167 reference statements)

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“…However, they contribute only to the feature-based algorithms—e.g., HOG, Scale-Invariant Feature Transform (SIFT), and Speeded Up Robust Features (SURF) —and deep learning-based solutions and did not cover direct techniques. Bavle et al [ 21 ] analyzed the situational awareness aspects in various SLAM and VSLAM applications and discussed their missing points. They could conclude that operating the lacking situational awareness features could enhance the performance of the current research works.…”

Section: Related Surveysmentioning

confidence: 99%

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

Tourani

Bavle

Sánchez-López

et al. 2022

Sensors

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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.

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Section: Related Surveysmentioning

confidence: 99%

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

Tourani

Bavle

Sánchez-López

et al. 2022

Sensors

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“…Yousif et al [17] discussed the elementary framework of VSLAM and summarized several mathematical problems to help readers make the best choice. Bavle et al [18] summarized the robot SLAM technology and pointed out the development trend of robot scene understanding. Starting from the fusion of vision and visual inertia, Servieres et al [19] reviewed and compared important methods and summarized excellent algorithms emerging in SLAM.…”

Section: Introductionmentioning

confidence: 99%

An Overview on Visual SLAM: From Tradition to Semantic

Chen

Shang

et al. 2022

Remote Sensing

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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.

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“…The constructed map can be used for mapping a novel environment or for the robot to plan for its mission with autonomy. Such a system can maintain stability, plan for its own movements, and reacts to dynamic changes in the surroundings without human intervention [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

A Review on Visual-SLAM: Advancements from Geometric Modelling to Learning-Based Semantic Scene Understanding Using Multi-Modal Sensor Fusion

Lai

2022

Sensors

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Simultaneous Localisation and Mapping (SLAM) is one of the fundamental problems in autonomous mobile robots where a robot needs to reconstruct a previously unseen environment while simultaneously localising itself with respect to the map. In particular, Visual-SLAM uses various sensors from the mobile robot for collecting and sensing a representation of the map. Traditionally, geometric model-based techniques were used to tackle the SLAM problem, which tends to be error-prone under challenging environments. Recent advancements in computer vision, such as deep learning techniques, have provided a data-driven approach to tackle the Visual-SLAM problem. This review summarises recent advancements in the Visual-SLAM domain using various learning-based methods. We begin by providing a concise overview of the geometric model-based approaches, followed by technical reviews on the current paradigms in SLAM. Then, we present the various learning-based approaches to collecting sensory inputs from mobile robots and performing scene understanding. The current paradigms in deep-learning-based semantic understanding are discussed and placed under the context of Visual-SLAM. Finally, we discuss challenges and further opportunities in the direction of learning-based approaches in Visual-SLAM.

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From SLAM to Situational Awareness: Challenges and Survey

Cited by 3 publications

References 157 publications

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

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

An Overview on Visual SLAM: From Tradition to Semantic

A Review on Visual-SLAM: Advancements from Geometric Modelling to Learning-Based Semantic Scene Understanding Using Multi-Modal Sensor Fusion

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