Semantic Mapping for Mobile Robots in Indoor Scenes: A Survey

Han, Xiaoning; Li, Shuailong; Wang, Xiaohui; Zhou, Weijia

doi:10.3390/info12020092

Cited by 16 publications

(13 citation statements)

References 48 publications

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“…These semantic tags can be identified and assigned based on prior knowledge, machine learning technology, or sensor data. Additionally, semantic maps may include relationshipships between locations to better describe the environment [43].…”

Section: Semantic Mapsmentioning

confidence: 99%

Map representations for mobile robot localization

Zhang,

2024

Fourth International Conference on Mechanical Engineering, Intelligent Manufacturing, and Automation Technology (MEMAT 2023)

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Localization is a prerequisite for mobile robots to achieve path planning, autonomous navigation, and decision-making. In localization, maps provide mobile robots with the ability to perceive and model the environment, so the choice of an appropriate map representation has a significant impact on the localization effect of mobile robots. From the perspective of usage matching, this paper divides map representations for mobile robot localization into four categories based on different focuses of describing the environment, which are metric maps, feature maps, topological maps, and semantic maps. The principles, characteristics, and classic cases of these representations are also analyzed in this paper. Furthermore, the applicable working conditions of each type of map representation are summarized to provide effective guidance for mobile robot localization.

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Section: Semantic Mapsmentioning

confidence: 99%

Map representations for mobile robot localization

Zhang,

2024

Fourth International Conference on Mechanical Engineering, Intelligent Manufacturing, and Automation Technology (MEMAT 2023)

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“…Semantic mapping [92] refers to the task of building semantically labeled maps of the environment (i.e., attaching semantic information to spatial entities), allowing a wider range of applications and capabilities beyond simple navigation and avoiding obstacles.…”

Section: ) Semantic Mapsmentioning

confidence: 99%

Deep Learning Techniques for Visual SLAM: A Survey

et al. 2023

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Visual Simultaneous Localization and Mapping (VSLAM) has attracted considerable attention in recent years. This task involves using visual sensors to localize a robot while simultaneously constructing an internal representation of its environment. Traditional VSLAM methods involve the laborious hand-crafted design of visual features and complex geometric models. As a result, they are generally limited to simple environments with easily identifiable textures. Recent years, however, have witnessed the development of deep learning techniques for VSLAM. This is primarily due to their capability of modeling complex features of the environment in a completely data-driven manner. In this paper, we present a survey of relevant deep learning-based VSLAM methods and suggest a new taxonomy for the subject. We also discuss some of the current challenges and possible directions for this field of study.

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“…Another key juncture of VSLAM and deep learning is the semantic map construction of SLAM, and most semantic VSLAM systems are based on this idea [230]. For a robot to understand the environment as well as a human and perform different tasks from one place to another requires a different skill than a geometric map can provide [231]. Robots should have the ability to have a human-centered understanding of their environment.…”

Section: Semantic With Mappingmentioning

confidence: 99%

An Overview on Visual SLAM: From Tradition to Semantic

et al. 2022

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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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Semantic Mapping for Mobile Robots in Indoor Scenes: A Survey

Cited by 16 publications

References 48 publications

Map representations for mobile robot localization

Map representations for mobile robot localization

Deep Learning Techniques for Visual SLAM: A Survey

An Overview on Visual SLAM: From Tradition to Semantic

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