Semantic Mapping with a Probabilistic Description Logic

Polastro, Rodrigo Bellizia; Corrêa, Fabiano Rogério; Cozman, Fábio Gagliardi; Okamoto, Jun

doi:10.1007/978-3-642-16138-4_7

Cited by 11 publications

(7 citation statements)

References 14 publications

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“…Besides, the authors weighted the advantages and disadvantages of using different sensors for the semantic labelling of places. Another work used Scale-invariant feature transform (SIFT) [53] features to characterize different areas [54]. This information was combined with a probabilistic description logic.…”

Section: Labellingmentioning

confidence: 99%

Semantic Information for Robot Navigation: A Survey

et al. 2020

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There is a growing trend in robotics for implementing behavioural mechanisms based on human psychology, such as the processes associated with thinking. Semantic knowledge has opened new paths in robot navigation, allowing a higher level of abstraction in the representation of information. In contrast with the early years, when navigation relied on geometric navigators that interpreted the environment as a series of accessible areas or later developments that led to the use of graph theory, semantic information has moved robot navigation one step further. This work presents a survey on the concepts, methodologies and techniques that allow including semantic information in robot navigation systems. The techniques involved have to deal with a range of tasks from modelling the environment and building a semantic map, to including methods to learn new concepts and the representation of the knowledge acquired, in many cases through interaction with users. As understanding the environment is essential to achieve high-level navigation, this paper reviews techniques for acquisition of semantic information, paying attention to the two main groups: human-assisted and autonomous techniques. Some state-of-the-art semantic knowledge representations are also studied, including ontologies, cognitive maps and semantic maps. All of this leads to a recent concept, semantic navigation, which integrates the previous topics to generate high-level navigation systems able to deal with real-world complex situations.

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Section: Labellingmentioning

confidence: 99%

Semantic Information for Robot Navigation: A Survey

et al. 2020

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“…They show in their work how spatial relationships based on a Fuzzy Ontology can be used to classify segments of a brain from an MRI image. Some work using DL and the relationship between the objects and indoor locations is presented in Polastro, Corrêa, Cozman, and Okamoto (2011). In this approach, a probabilistic DL language CRALC (Credal ALC) is used to model the ontology of an office environment.…”

Section: Related Workmentioning

confidence: 99%

Towards Coordinated Multirobot Missions for Lunar Sample Collection in an Unknown Environment

Eich

Hartanto

Kasperski

et al. 2013

Journal of Field Robotics

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In this paper, the recent results of the space project IMPERA are presented. The goal of IMPERA is the development of a multirobot planning and plan execution architecture with a focus on a lunar sample collection scenario in an unknown environment. We describe the implementation and verification of different modules that are integrated into a distributed system architecture. The modules include a mission planning approach for a multirobot system and modules for task and skill execution within a lunar use‐case scenario. The skills needed for the test scenario include cooperative exploration and mapping strategies for an unknown environment, the localization and classification of sample containers using a novel approach of semantic perception, and the skill of transporting sample containers to a collection point using a mobile manipulation robot. Additionally, we present our approach of a reliable communication framework that can deal with communication loss during the mission. Several modules are tested within several experiments in the domain of planning and plan execution, communication, coordinated exploration, perception, and object transportation. An overall system integration is tested on a mission scenario experiment using three robots.

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“…The literature on place labeling methods for robot navigation is extensive [ 7 , 8 , 9 ]. A trend is to identify regions of interest in the environment, such as floor, walls and doors [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

A Semantic Labeling of the Environment Based on What People Do

Crespo

Gómez

Hernandez

et al. 2017

Sensors

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Abstract:In this work, a system is developed for semantic labeling of locations based on what people do. This system is useful for semantic navigation of mobile robots. The system differentiates environments according to what people do in them. Background sound, number of people in a room and amount of movement of those people are items to be considered when trying to tell if people are doing different actions. These data are sampled, and it is assumed that people behave differently and perform different actions. A support vector machine is trained with the obtained samples, and therefore, it allows one to identify the room. Finally, the results are discussed and support the hypothesis that the proposed system can help to semantically label a room.

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Semantic Mapping with a Probabilistic Description Logic

Cited by 11 publications

References 14 publications

Semantic Information for Robot Navigation: A Survey

Semantic Information for Robot Navigation: A Survey

Towards Coordinated Multirobot Missions for Lunar Sample Collection in an Unknown Environment

A Semantic Labeling of the Environment Based on What People Do

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