New PDF release: Semantic Labeling of Places with Mobile Robots

By Óscar Martinez Mozos

ISBN-10: 3642112099

ISBN-13: 9783642112096

During the final years there was an expanding curiosity within the zone of provider robots. less than this classification we discover robots operating in initiatives reminiscent of aged care, guiding, place of work and household counsel, inspection, and lots of extra. provider robots often paintings in indoor environments designed for people, with places of work and homes being the most normal examples. those environments are usually divided into areas with diverse functionalities like corridors, rooms or doors. the power to profit such semantic different types from sensor information allows a cellular robotic to increase its illustration of our surroundings, and to enhance its services. for example, ordinary language phrases like hall or room can be utilized to point the placement of the robotic in a extra intuitive manner whilst speaking with humans.

This e-book provides a number of ways to let a cellular robotic to categorize locations in indoor environments. the types are indicated by means of phrases which symbolize the various areas in those environments. the target of this paintings is to allow cellular robots to understand the spatial divisions in indoor environments in a similar fashion as humans do. this is often an engaging breakthrough to the matter of relocating the belief of robots in the direction of the conception of people. Many methods brought during this e-book come from the world of trend popularity and type. The utilized tools were tailored to unravel the categorical challenge of position reputation. during this regard, this paintings is an invaluable connection with scholars and researchers who are looking to introduce category innovations to aid remedy comparable difficulties in cellular robotics.

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Extra resources for Semantic Labeling of Places with Mobile Robots

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However, common configurations on real mobile robots have only a laser covering 180o in front of the robot. This situation is depicted in Fig. 7(b). In these last cases we propose to maintain a local map around the robot when classifying a pose of the robot during a trajectory. This local map can be updated during the movements of the robot, and then used to simulate the rear laser beams. An example of a local map is shown in Fig. 7(c). In our case, we maintain a sparse local map around the robot.

We restrict ourselves to the case of discrete variables, that is, each variable yi ∈ Y corresponds to a set of K possible labels yi ∈ {1, . . , K}. Thus, we define a Markov random field as an undirected graph G = (V, E) where the set of nodes V represents discrete variables, and the edges E refer to the relations between them [22]. An AMN can be divided into a subset of cliques Q, where each clique q ∈ Q is associated with a subset Yq ∈ Y. The nodes in a clique Yq form a fully connected subgraph.

Otherwise the example is passed to the next classifier in the list. The structure of such a decision list is shown in Fig. 4. 4 Simple Features from Sensor Range Data Binary Classifier1 h(x) = 0 h(x) = 1 Class1 . . 19 Binary h(x) = 0 ClassifierK−1 ClassK h(x) = 1 ClassK−1 Fig. 4 A decision list for K classes using binary classifiers. One important question in the context of a sequential classifier is the order in which the individual binary classifiers are arranged. This order can have a major influence on the overall classification performance, because the individual classifiers typically have different accuracies.

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Semantic Labeling of Places with Mobile Robots by Óscar Martinez Mozos


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