A bayesian conceptualization of space for mobile robots

Vasudevan, Sridhar R.; Siegwart, Roland

doi:10.1109/iros.2007.4399099

Cited by 10 publications

(4 citation statements)

References 15 publications

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“…Semantic navigation is related to classification methods of the environment object and places, which provides more effectiveness, as is described in Vasudevan & Siegwart [32]. An example of place identification using a Naive Bayes Classifier to infer place identification is shown in Duda et al [33] and Kollar & Roy [34].…”

Section: Semantical Navigationmentioning

confidence: 99%

Mobile Robot Navigation in Indoor Environments: Geometric, Topological, and Semantic Navigation

Barber¹,

Crespo²,

Gómez³

et al. 2019

Applications of Mobile Robots

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T h eo b j e c t i v eo ft h ec h a p t e ri st os h o wc u r r ent trends in robot navigation systems related to indoor environments. Navigation systems depend on the level of abstraction of the environment representation. The three main techniques for representing the environment will be described: geometric, topological, and semantic. The geometric representation of the environment is closer to the sensor and actuator world and it is the best one to perform local navigation. Topological representation of the environment uses graphs to model the environment and it is used in large navigation tasks. The semantic representation is the most abstractr e p r e s e n t a t i o nm o d e la n da d d sc o n c e p t s such as utilities or meanings of the environment elements in the map representation. In addition, regardless of the representation used for navigation, perception plays a significant role in terms of understanding and moving through the environment.

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Section: Semantical Navigationmentioning

confidence: 99%

Mobile Robot Navigation in Indoor Environments: Geometric, Topological, and Semantic Navigation

Barber¹,

Crespo²,

Gómez³

et al. 2019

Applications of Mobile Robots

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“…The space where a virtual agent is located will return a bedroom as a result of inference by the classifier. NBC-based classification enables more accurate classification with more classification standards [10]. More accurate classification results can be acquired by collecting and classifying the probability depending on the number of objects and the probability depending on the distance, as well as the appearance ratio of objects [11].…”

Section: Related Workmentioning

confidence: 99%

Automated Space Classification for Network Robots in Ubiquitous Environments

Choi¹,

Cho

Chu

et al. 2015

Journal of Sensors

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Network robots provide services to users in smart spaces while being connected to ubiquitous instruments through wireless networks in ubiquitous environments. For more effective behavior planning of network robots, it is necessary to reduce the state space by recognizing a smart space as a set of spaces. This paper proposes a space classification algorithm based on automatic graph generation and naive Bayes classification. The proposed algorithm first filters spaces in order of priority using automatically generated graphs, thereby minimizing the number of tasks that need to be predefined by a human. The filtered spaces then induce the final space classification result using naive Bayes space classification. The results of experiments conducted using virtual agents in virtual environments indicate that the performance of the proposed algorithm is better than that of conventional naive Bayes space classification.

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“…The first example scenario shown in Figure 1 covers the area of scene analysis for manipulation of objects and for semantic space mapping tasks [11]. In this paper the main points to be considered are: 1. the scene is captured by sensors (e.g.…”

Section: Application Scenariosmentioning

confidence: 99%

“…Note, that for illustrative reasons the physically plausible area is broadened instead of being infinitesimal small. The density value is set to zero, and the whole distribution is normalized to sum up to one (11). …”

Section: Virtual State Updatementioning

confidence: 99%

Local dependency analysis in probabilistic scene estimation

Grundmann

Eidenberger

Zöllner

2008

2008 5th International Symposium on Mechatronics and Its Applications

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A general solution to the problem of jointly estimating the state of multiple entities is regarded computationally challenging at the time. Most solutions are based on the application of wide assumptions of independence. In many situations and constellations of entities, this is sufficient and leads to high quality results. In some situations as occlusion for instance the assumption of independence is violated heavily resulting in considerable errors. The proposed approach considers local dependencies, allowing to increase the accuracy of the estimation punctually, depending on application requirements, such as high precision localization for grasping operations or rough precision for semantic localization.

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A bayesian conceptualization of space for mobile robots

Cited by 10 publications

References 15 publications

Mobile Robot Navigation in Indoor Environments: Geometric, Topological, and Semantic Navigation

Mobile Robot Navigation in Indoor Environments: Geometric, Topological, and Semantic Navigation

Automated Space Classification for Network Robots in Ubiquitous Environments

Local dependency analysis in probabilistic scene estimation

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