A Survey of Ontologies for Simultaneous Localization and Mapping in Mobile Robots

Cornejo-Lupa, Maria A.; Ticona-Herrera, Regina; Cardinale, Yudith; Barrios-Aranibar, Dennis

doi:10.1145/3408316

Cited by 9 publications

(8 citation statements)

References 30 publications

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“…SLAM domain experts also act as a source and support for conceptualization, since they provide their terminology. Section 2 and the previous work presented in [7], reflect some results of this familiarization phase.…”

Section: Context Familiarizationmentioning

confidence: 75%

“…For example, despite using different techniques or sensors, robots can store and share the knowledge acquired with the same ontology: an aerial robot in a 3D spatial scenario could share the location of features with a terrestrial robot in a 2D spatial scenario. Some studies have formulated ontologies to partially model the information related to some aspects of SLAM, as shown in the studies presented in [6,7], that propose a categorization of the knowledge domain of SLAM and compare state-of-the-art SLAM ontologies. Those studies show that most SLAM ontologies are focused on the knowledge related to the SLAM final result (i.e., the maps) and considering the SLAM problem as a static process [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…In total, from the categorization of SLAM knowledge, there exist 13 subcategories that represent the aspects that might be considered when modeling the SLAM problem. In a previous work [7], the most popular and recent SLAM ontologies up to 2020 are revised, classifying them according to the proposed categorization. In this section, that review is updated up to 2021 and it is presented a brief description on how the existing ontologies model partial aspects of the knowledge associated with SLAM, according to the categorization considered.…”

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confidence: 99%

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OntoSLAM: An Ontology for Representing Location and Simultaneous Mapping Information for Autonomous Robots

et al. 2021

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Autonomous robots are playing an important role to solve the Simultaneous Localization and Mapping (SLAM) problem in different domains. To generate flexible, intelligent, and interoperable solutions for SLAM, it is a must to model the complex knowledge managed in these scenarios (i.e., robots characteristics and capabilities, maps information, locations of robots and landmarks, etc.) with a standard and formal representation. Some studies have proposed ontologies as the standard representation of such knowledge; however, most of them only cover partial aspects of the information managed by SLAM solutions. In this context, the main contribution of this work is a complete ontology, called OntoSLAM, to model all aspects related to autonomous robots and the SLAM problem, towards the standardization needed in robotics, which is not reached until now with the existing SLAM ontologies. A comparative evaluation of OntoSLAM with state-of-the-art SLAM ontologies is performed, to show how OntoSLAM covers the gaps of the existing SLAM knowledge representation models. Results show the superiority of OntoSLAM at the Domain Knowledge level and similarities with other ontologies at Lexical and Structural levels. Additionally, OntoSLAM is integrated into the Robot Operating System (ROS) and Gazebo simulator to test it with Pepper robots and demonstrate its suitability, applicability, and flexibility. Experiments show how OntoSLAM provides semantic benefits to autonomous robots, such as the capability of inferring data from organized knowledge representation, without compromising the information for the application and becoming closer to the standardization needed in robotics.

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Section: Context Familiarizationmentioning

confidence: 75%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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OntoSLAM: An Ontology for Representing Location and Simultaneous Mapping Information for Autonomous Robots

et al. 2021

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“…EMONTO can be extended with other specific domain ontologies representing entities with which the emotion can be related. The detected emotion can be used for many purposes, such as accordingly designing the reactions and actions of robots or combining the semantic information of emotions with other ontologies related to a robot’s tasks (e.g., SLAM (Simultaneous Localization And Mapping), navigation, and perception) [ 16 ] or with domain-specific ontologies related to the environment where robots work (e.g., museums, restaurants, and hospitals) [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Emotion Detection for Social Robots Based on NLP Transformers and an Emotion Ontology

Graterol

Díaz-Amado

Cardinale

et al. 2021

Sensors

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For social robots, knowledge regarding human emotional states is an essential part of adapting their behavior or associating emotions to other entities. Robots gather the information from which emotion detection is processed via different media, such as text, speech, images, or videos. The multimedia content is then properly processed to recognize emotions/sentiments, for example, by analyzing faces and postures in images/videos based on machine learning techniques or by converting speech into text to perform emotion detection with natural language processing (NLP) techniques. Keeping this information in semantic repositories offers a wide range of possibilities for implementing smart applications. We propose a framework to allow social robots to detect emotions and to store this information in a semantic repository, based on EMONTO (an EMotion ONTOlogy), and in the first figure or table caption. Please define if appropriate. an ontology to represent emotions. As a proof-of-concept, we develop a first version of this framework focused on emotion detection in text, which can be obtained directly as text or by converting speech to text. We tested the implementation with a case study of tour-guide robots for museums that rely on a speech-to-text converter based on the Google Application Programming Interface (API) and a Python library, a neural network to label the emotions in texts based on NLP transformers, and EMONTO integrated with an ontology for museums; thus, it is possible to register the emotions that artworks produce in visitors. We evaluate the classification model, obtaining equivalent results compared with a state-of-the-art transformer-based model and with a clear roadmap for improvement.

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“…SLAM deals with the necessity of building a map of an environment, while simultaneously determining the location of the robot within this map. In a previous study (Cornejo et al, 2020b), we surveyed the most popular and recent SLAM ontologies, classifying them according to the type of knowledge modeled. In this work, we evaluate and compare them, including our proposed ontology for SLAM (OntoSLAM), following our extended methodological process.…”

Section: Introductionmentioning

confidence: 99%

Application of a methodological approach to compare ontologies

Cardinale

Cornejo-Lupa

Gala

et al. 2021

IJWIS

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Purpose This study aims to the OQuaRE quality model to the developed methodology. Design/methodology/approach Ontologies are formal, well-defined and flexible representations of knowledge related to a specific domain. They provide the base to develop efficient and interoperable solutions. Hence, a proliferation of ontologies in many domains is unleashed. Then, it is necessary to define how to compare such ontologies to decide which one is the most suitable for the specific needs of users/developers. As the emerging development of ontologies, several studies have proposed criteria to evaluate them. Findings In a previous study, the authors propose a methodological process to qualitatively and quantitatively compare ontologies at Lexical, Structural and Domain Knowledge levels, considering correctness and quality perspectives. As the evaluation methods of the proposal are based on a golden-standard, it can be customized to compare ontologies in any domain. Practical implications To show the suitability of the proposal, the authors apply the methodological approach to conduct comparative studies of ontologies in two different domains, one in the robotic area, in particular for the simultaneous localization and mapping (SLAM) problem; and the other one, in the cultural heritage domain. With these cases of study, the authors demonstrate that with this methodological comparative process, we are able to identify the strengths and weaknesses of ontologies, as well as the gaps still needed to fill in the target domains. Originality/value Using these metrics and the quality model from OQuaRE, the authors are incorporating a standard of software engineering at the quality validation into the Semantic Web.

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A Survey of Ontologies for Simultaneous Localization and Mapping in Mobile Robots

Cited by 9 publications

References 30 publications

OntoSLAM: An Ontology for Representing Location and Simultaneous Mapping Information for Autonomous Robots

OntoSLAM: An Ontology for Representing Location and Simultaneous Mapping Information for Autonomous Robots

Emotion Detection for Social Robots Based on NLP Transformers and an Emotion Ontology

Application of a methodological approach to compare ontologies

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