Household robots look and learn

Kröse, B.J.A.; Bunschoten, R.; St, Hagen; Terwijn, B.; Vlassis, Nikos

doi:10.1109/mra.2004.1371608

Cited by 32 publications

(8 citation statements)

References 9 publications

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“…A widely extended method is PCA (Principal Components Analysis). One example is the database created in (Kröse, Bunschoten, Hagen, Terwijn and Vlassis, 2004). Uenoara and Kanade (1998) studied the problem of rotation in the plane in which the robot moves, using a set of rotated images.…”

Section: Introductionmentioning

confidence: 99%

ROBUST METHODS FOR ROBOT LOCALIZATION UNDER CHANGING ILLUMINATION CONDITIONS - Comparison of Different Filtering Techniques

Rojo¹,

Payá²,

Reinoso

et al. 2010

Proceedings of the 2nd International Conference on Agents and Artificial Intelligence

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The use of omnidirectional systems provides us with rich visual information that allows us to create appearance-based dense maps. This map can be composed of several panoramic images taken from different positions in the environment. When the map contains only visual information, it will depend heavily on the conditions of the environment lighting. Therefore we get different visual information depending on the time of day when the map is created, the state of artificial lighting in the environment, or any other circumstance that causes a change in the illumination of the scene. To obtain a robust map against changes in the illumination of the environment we apply different filters on the panoramic images. After that, we use some compression methods that allow us to reduce the amount of information stored. We have conducted a comprehensive experimentation to study which type of filter best adapts to changing lighting conditions.

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Section: Introductionmentioning

confidence: 99%

ROBUST METHODS FOR ROBOT LOCALIZATION UNDER CHANGING ILLUMINATION CONDITIONS - Comparison of Different Filtering Techniques

Rojo¹,

Payá²,

Reinoso

et al. 2010

Proceedings of the 2nd International Conference on Agents and Artificial Intelligence

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“…To solve it, in some occasions, a feature selection is accomplished to determine the relationship between the images (Boorj et al, 2006). When working with the whole images, the complexity of the problem can be reduced by means of the PCA (Principal Components Analysis) subspace as in (Kröse et al, 2004) or (Maeda et al, 1997), where through PCA techniques a database is created using a set of views with a probabilistic approach for the localization. Other works have shown how this subspace reduction can be applied to other tasks in robotics, such in loop closure detection for SLAM tasks (Newman et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Appearance-Based Processes in Multi-Robot Navigation

Payá¹,

Reinoso²,

Aznar³

et al. 2008

Recent Advances in Multi Robot Systems

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“…It is just needed a teaching step, where the route to follow is learned and a navigation step, where the second robot follows the route comparing the current sensory information with the data stored in the database. Also, the complexity of the problem can be reduced working in the PCA (Principal Components Analysis) subspace as in [5], where through PCA techniques, a database is created using a set of views with a probabihstic approach for the localization [1]. However, other approaches suggest that these processes could be achieved just comparing the general visual information in the images, without extracting any feature, what would be useful for comphcated scenes in the real world in which appropriate models for recognition are difficult to create.…”

Section: Introductionmentioning

confidence: 99%

Improving Appearance-Based Following Routes with a Probabilistic Approach

Payá¹,

Pedrero²,

Reinoso³

et al. 2008

AIP Conference Proceedings

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This work presents an appearance-based approach to route following in multi-robot systems, using the information captured by a conventional forward-looking camera. In the teaching phase, the most relevant information along the route is stored using incremental Principal Components Analysis (PCA), what means that the follower robot can begin the route while the leader is still recording it. The follower robot makes an auto-localization process, comparing the current view with the information stored in the database, using a probabilistic approach. Then, a fuzzy controller is in charge of calculating the speed and turning to follow the route. Experimental results have shown the robustness of the algorithms in an office environment.

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Household robots look and learn

Cited by 32 publications

References 9 publications

ROBUST METHODS FOR ROBOT LOCALIZATION UNDER CHANGING ILLUMINATION CONDITIONS - Comparison of Different Filtering Techniques

ROBUST METHODS FOR ROBOT LOCALIZATION UNDER CHANGING ILLUMINATION CONDITIONS - Comparison of Different Filtering Techniques

Appearance-Based Processes in Multi-Robot Navigation

Improving Appearance-Based Following Routes with a Probabilistic Approach

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