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11 Result(s)
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Book
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Chapter
Introduction
Robots need to understand their environment in order to be able to perform different tasks within it. A robot’s interface with the external world is usually composed of several sensors that gather data. How to...
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Chapter
Probabilistic Semantic Classification of Trajectories
The approaches described in previous chapters are able to classify static observations using a mobile robot. However, mobile robots are dynamic agents that move along different trajectories. When operating in ...
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Chapter
Conceptual Spatial Representation of Indoor Environments
In the last years, there has been an increasing interest in service robots, such as domestic or elderly care robots, whose purpose is to assist people in human-like environments. These service robots have to i...
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Chapter
Conclusion
This book presented different approaches for adding semantic information to the representations of indoor environments.We concentrated on extending the information in the maps created by a mobile robot with la...
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Chapter
Supervised Learning
In a supervised learning task we are interested in finding a function that maps a set of given examples into a set of classes or categories. This function, called classifier, will be used later to classify new ex...
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Chapter
Semantic Information in Exploration and Localization
The work presented in the previous chapters showed how to augment the representation of indoor environments using semantic information about places. In this chapter we describe howrobots can use the intrinsic ...
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Chapter
Semantic Information in Sensor Data
So far, we have seen how to augment the maps representing environments with semantic information. This additional information was obtained by classifying the laser range data obtained by a mobile robot into so...
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Chapter
Semantic Learning of Places from Range Data
Building accurate maps of indoor environments is one of the typical problems in mobile robotics. In this task, a robot moves along a trajectory while gathering information with sensors. Typical maps represent ...
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Chapter
Topological Map Extraction with Semantic Information
In the previous chapter we saw how a robot can classify its pose in an indoor environment into a semantic class. The different semantic classes represented typical divisions of the environment such as corridor...
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Chapter and Conference Paper
Using AdaBoost for Place Labeling and Topological Map Building
Indoor environments can typically be divided into places with different functionalities like corridors, kitchens, offices, or seminar rooms. We believe that the ability to learn such semantic categories from s...
