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Collaborative Mobile Robotics for Semantic Mapping: A Survey

APPLIED SCIENCES-BASEL (2022)

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Article Robotics

Perception-Based Temporal Logic Planning in Uncertain Semantic Maps

Yiannis Kantaros et al.

Summary: This article addresses a multi-robot planning problem in environments with partially unknown semantics. It introduces a perception-based LTL planning algorithm that generates open-loop control policies updated online to adapt to a continuously learned semantic map, enabling robots to accomplish collaborative tasks.

IEEE TRANSACTIONS ON ROBOTICS (2022)

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Article Automation & Control Systems

Sharing visual-inertial data for collaborative decentralized simultaneous localization and mapping

Rodolphe Dubois et al.

Summary: This article proposes three methods to share visual-inertial information and a common collaborative SLAM architecture. Experimental results show that these methods can improve the trajectory estimation accuracy of the robots.

ROBOTICS AND AUTONOMOUS SYSTEMS (2022)

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Article Automation & Control Systems

COSEM: Collaborative Semantic Map Matching Framework for Autonomous Robots

Yufeng Yue et al.

Summary: This article proposes a novel approach named COSEM for estimating the relative transformation between robots. COSEM performs multimodal information fusion, semantic data association, and optimization in a unified framework. Evaluations show its effectiveness in improving accuracy, convergence, and robustness.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2022)

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Article Computer Science, Information Systems

Hybrid offline and online task planning for service robot using object-level semantic map and probabilistic inference

Zhongli Wang et al.

Summary: In this paper, a hybrid offline and online task planning strategy is proposed to enable service robots to intelligently plan a whole task execution sequence and handle task failure in the presence of object occlusion and incomplete home environment information. Experimental results demonstrate that this approach effectively enhances the intelligence of task planning and improves the robustness and efficiency of robot task execution.

INFORMATION SCIENCES (2022)

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Article Computer Science, Hardware & Architecture

A brief survey on RGB-D semantic segmentation using deep learning*

Changshuo Wang et al.

Summary: Semantic segmentation is the process of linking each pixel in an image to a class label, recognizing objects based on color and texture for easier evaluation. While RGB image methods perform well, RGB-D semantic segmentation with depth information has been proven to achieve better results.

DISPLAYS (2021)

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Article Computer Science, Artificial Intelligence

ViMantic, a distributed robotic architecture for semantic mapping in indoor environments

D. Fernandez-Chaves et al.

Summary: Semantic maps provide meta-information about robot workspaces, allowing for the execution of high-level robotic tasks efficiently. ViMantic is a novel semantic mapping architecture that includes formal models, techniques for processing sensory information, distributed execution capabilities, user interface, and public availability. The suitability of ViMantic has been confirmed through experiments with Robot@Home, showing satisfactory results for automatic population, execution times, and memory size of the resulting semantic maps.

KNOWLEDGE-BASED SYSTEMS (2021)

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Proceedings Paper Automation & Control Systems

Multi-Robot Distributed Semantic Mapping in Unfamiliar Environments through Online Matching of Learned Representations

Stewart Jamieson et al.

Summary: The proposed solution addresses the challenges of multi-robot distributed semantic mapping in novel environments by having each robot learn an unsupervised semantic scene model online and using a multiway matching algorithm to identify consistent sets of matches. This approach leads to higher quality global maps compared to existing state-of-the-art systems, even with the fusion of more local maps.

2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021) (2021)

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Review Computer Science, Information Systems

Semantic Mapping for Mobile Robots in Indoor Scenes: A Survey

Xiaoning Han et al.

Summary: Sensing and mapping surroundings is crucial for a mobile robot. By attaching semantic information to a geometric map, known as a semantic map, robots can act in accordance with human rules, plan and execute advanced tasks, and communicate with humans on a conceptual level. This survey reviews methods for semantic mapping in indoor scenes and discusses challenges and potential future directions in implementing semantic maps for robots.

INFORMATION (2021)

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Article Robotics

Building semantic grid maps for domestic robot navigation

Xianyu Qi et al.

INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS (2020)

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Article Chemistry, Multidisciplinary

Semantic Information for Robot Navigation: A Survey

Jonathan Crespo et al.

APPLIED SCIENCES-BASEL (2020)

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Article Robotics

CLEAR: A Consistent Lifting, Embedding, and Alignment Rectification Algorithm for Multiview Data Association

Kaveh Fathian et al.

IEEE TRANSACTIONS ON ROBOTICS (2020)

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Review Geochemistry & Geophysics

Linking Points With Labels in 3D: A Review of Point Cloud Semantic Segmentation

Yuxing Xie et al.

IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE (2020)

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Proceedings Paper Automation & Control Systems

A Hierarchical Framework for Collaborative Probabilistic Semantic Mapping

Yufeng Yue et al.

2020 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA) (2020)

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Article Automation & Control Systems

Time-Constrained Exploration Using Toposemantic Spatial Models A Reproducible Approach to Measurable Robotics

Jose Luis Susa Rincon et al.

IEEE ROBOTICS & AUTOMATION MAGAZINE (2019)

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Article Robotics

AgriColMap: Aerial-Ground Collaborative 3D Mapping for Precision Farming

Ciro Potena et al.

IEEE ROBOTICS AND AUTOMATION LETTERS (2019)

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Article Computer Science, Information Systems

A Review of Deep Learning-Based Semantic Segmentation for Point Cloud

Jiaying Zhang et al.

IEEE ACCESS (2019)

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Article Engineering, Electrical & Electronic

Hierarchical Probabilistic Fusion Framework for Matching and Merging of 3-D Occupancy Maps

Yufeng Yue et al.

IEEE SENSORS JOURNAL (2018)

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Article Computer Science, Artificial Intelligence

Relational Model for Robotic Semantic Navigation in Indoor Environments

J. Crespo et al.

JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS (2017)

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Article Robotics

3-D Map Merging on Pose Graphs

Taigo Maria Bonanni et al.

IEEE ROBOTICS AND AUTOMATION LETTERS (2017)

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Article Computer Science, Artificial Intelligence

Modeling curiosity in a mobile robot for long-term autonomous exploration and monitoring

Yogesh Girdhar et al.

AUTONOMOUS ROBOTS (2016)

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Review Robotics

Multiple-Robot Simultaneous Localization and Mapping: A Review

Sajad Saeedi et al.

JOURNAL OF FIELD ROBOTICS (2016)

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Article Robotics

Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age

Cesar Cadena et al.

IEEE TRANSACTIONS ON ROBOTICS (2016)

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Review Robotics

Extracting Semantic Information from Visual Data: A Survey

Qiang Liu et al.

ROBOTICS (2016)

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Article Automation & Control Systems

Semantic mapping for mobile robotics tasks: A survey

Ioannis Kostavelis et al.

ROBOTICS AND AUTONOMOUS SYSTEMS (2015)

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Article Telecommunications

Building a grid-semantic map for the navigation of service robots through human-robot interaction

Cheng Zhao et al.

DIGITAL COMMUNICATIONS AND NETWORKS (2015)

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Article Computer Science, Artificial Intelligence

Enhanced maximum likelihood grid map with reprocessing incorrect sonar measurements

Kyoungmin Lee et al.

AUTONOMOUS ROBOTS (2013)

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Article Computer Science, Artificial Intelligence

OctoMap: an efficient probabilistic 3D mapping framework based on octrees

Armin Hornung et al.

AUTONOMOUS ROBOTS (2013)

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Article Robotics

Knowledge acquisition through human-robot multimodal interaction

Gabriele Randelli et al.

INTELLIGENT SERVICE ROBOTICS (2013)

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Article Robotics

Semantic mapping using mobile robots

Denis F. Wolf et al.

IEEE TRANSACTIONS ON ROBOTICS (2008)

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Article Automation & Control Systems

Towards semantic maps for mobile robots

Andreas Nuechter et al.

ROBOTICS AND AUTONOMOUS SYSTEMS (2008)

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Article Automation & Control Systems

Robot task planning using semantic maps

Cipriano Galindo et al.

ROBOTICS AND AUTONOMOUS SYSTEMS (2008)

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Article Automation & Control Systems

Conceptual spatial representations for indoor mobile robots

H. Zender et al.

ROBOTICS AND AUTONOMOUS SYSTEMS (2008)

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Article Robotics

Improved techniques for grid mapping with Rao-Blackwellized particle filters

Giorgio Grisetti et al.

IEEE TRANSACTIONS ON ROBOTICS (2007)

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Article Robotics

Topological map merging

WH Huang et al.

INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH (2005)

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