A Semantic Labeling of the Environment Based on What People Do

Crespo, Jonathan; Gómez, Clara; Hernandez, Alejandra C.; Barber, Ramón

doi:10.3390/s17020260

Cited by 2 publications

(5 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are systems developed to work on mobile robots, performing navigation tasks and environment categorization, where accuracy is not the most important factor. The examples are systems for semantic navigation of mobile robots [32,33]. Better recognition accuracy must be provided, e.g., by multisensory system for service robots used in automatic harvesting of fruits [34].…”

Section: Shape Recognition and Classification Methodsmentioning

confidence: 99%

Object Identification for Task-Oriented Communication with Industrial Robots

Rogowski

Skrobek

2020

Sensors

View full text Add to dashboard Cite

To make the human-robot collaboration effective, it may be necessary to provide robots with “senses” like vision and hearing. Task-oriented man-machine speech communication often relies on the use of abstract terms to describe objects. Therefore it is necessary to correctly map those terms into images of proper objects in a camera’s field of view. This paper presents the results of our research in this field. A novel method for contour identification, based on flexible editable contour templates (FECT), has been developed. We demonstrate that existing methods are not appropriate for this purpose because it is difficult to formulate general rules that humans employ to rank shapes into proper classes. Therefore, the rules for shape classification should be individually formulated by the users for each application. Our aim was to create appropriate tool facilitating formulation of those rules as it could potentially be a very labor-intensive task. The core of our solution is FCD (flexible contour description) format for description of flexible templates. Users will be able to create and edit flexible contour templates, and thus, adjust image recognition systems to their needs, in order to provide task-oriented communication between humans and robots.

show abstract

Section: Shape Recognition and Classification Methodsmentioning

confidence: 99%

Object Identification for Task-Oriented Communication with Industrial Robots

Rogowski

Skrobek

2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Authors claimed that the depth information of the RGB-D sensor has significantly improved the loop closure as well as feature matching, and that provides a better spatial cognitive map. Apart from these three categories, other authors proposed adding a fourth one that tries to classify places using information about the actions that people perform in the environment [29].…”

Section: Fully Automated Information Acquisitionmentioning

confidence: 99%

“…A complete system can be found in Luo et al [45] where authors performed bulk segmentation, detection of head and shoulders and a time refinement. Additionally, the people detection algorithm used in Crespo et al [29] included the detection of legs proposed by Aguirre et al [46]. The diagram of the system is represented in Figure 2.…”

Section: Understanding the Environmentmentioning

confidence: 99%

“…[57]. More recently, another work includes a place classification method based on people activities, resulting on the labelling of six environments (cafeteria, library, corridor, exhibition room, conference room and indoor soccer field) [29]. These approaches include a partition of unsupervised places, for instance segmenting the environment to have distinguishable places on top of a topological map [111], or creating an ontology that relates a conceptual hierarchy to the spatial hierarchy needed by a lower-level navigator [19].…”

Section: Principles Of Semantic Navigationmentioning

confidence: 99%

“…It is worth noticing that some works follow a hybrid scheme, using both information acquisition through interaction with humans as well as autonomous information acquisition processes. [13] NO NO YES NO YES [24] NO NO YES NO NO [25] NO NO YES YES NO [28] NO NO YES NO YES Crespo et al, included techniques designed to recognize patterns in the actions of individuals to label a room, that is, labelling a place depending on what people do in them [29]. Kollar and Roy relied on probabilistic methods based on Naive Bayes for the classification of places, obtaining the probabilities of objects locations [3].…”

mentioning

confidence: 99%

See 2 more Smart Citations

Semantic Information for Robot Navigation: A Survey

et al. 2020

Self Cite

View full text Add to dashboard Cite

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.

show abstract

A Semantic Labeling of the Environment Based on What People Do

Cited by 2 publications

References 34 publications

Object Identification for Task-Oriented Communication with Industrial Robots

Object Identification for Task-Oriented Communication with Industrial Robots

Semantic Information for Robot Navigation: A Survey

Contact Info

Product

Resources

About