46,904 research outputs found
Supervised semantic labeling of places using information extracted from sensor data
Indoor environments can typically be divided into places with different functionalities like corridors, rooms or doorways. The ability to learn such semantic categories from sensor data enables a mobile robot to extend the representation of the environment facilitating interaction with humans. As an example, natural language terms like “corridor” or “room” can be used to communicate the position of the robot in a map in a more intuitive way. In this work, we first propose an approach based on supervised learning to classify the pose of a mobile robot into semantic classes. Our method uses AdaBoost to boost simple features extracted from sensor range data into a strong classifier. We present two main applications of this approach. Firstly, we show how our approach can be utilized by a moving robot for an online classification of the poses traversed along its path using a hidden Markov model. In this case we additionally use as features objects extracted from images. Secondly, we introduce an approach to learn topological maps from geometric maps by applying our semantic classification procedure in combination with a probabilistic relaxation method. Alternatively, we apply associative Markov networks to classify geometric maps and compare the results with a relaxation approach. Experimental results obtained in simulation and with real robots demonstrate the effectiveness of our approach in various indoor environments
Global and local Complexity in weakly chaotic dynamical systems
In a topological dynamical system the complexity of an orbit is a measure of
the amount of information (algorithmic information content) that is necessary
to describe the orbit. This indicator is invariant up to topological
conjugation. We consider this indicator of local complexity of the dynamics and
provide different examples of its behavior, showing how it can be useful to
characterize various kind of weakly chaotic dynamics. We also provide criteria
to find systems with non trivial orbit complexity (systems where the
description of the whole orbit requires an infinite amount of information). We
consider also a global indicator of the complexity of the system. This global
indicator generalizes the topological entropy, taking into account systems were
the number of essentially different orbits increases less than exponentially.
Then we prove that if the system is constructive (roughly speaking: if the map
can be defined up to any given accuracy using a finite amount of information)
the orbit complexity is everywhere less or equal than the generalized
topological entropy. Conversely there are compact non constructive examples
where the inequality is reversed, suggesting that this notion comes out
naturally in this kind of complexity questions.Comment: 23 page
Finite Boolean Algebras for Solid Geometry using Julia's Sparse Arrays
The goal of this paper is to introduce a new method in computer-aided
geometry of solid modeling. We put forth a novel algebraic technique to
evaluate any variadic expression between polyhedral d-solids (d = 2, 3) with
regularized operators of union, intersection, and difference, i.e., any CSG
tree. The result is obtained in three steps: first, by computing an independent
set of generators for the d-space partition induced by the input; then, by
reducing the solid expression to an equivalent logical formula between Boolean
terms made by zeros and ones; and, finally, by evaluating this expression using
bitwise operators. This method is implemented in Julia using sparse arrays. The
computational evaluation of every possible solid expression, usually denoted as
CSG (Constructive Solid Geometry), is reduced to an equivalent logical
expression of a finite set algebra over the cells of a space partition, and
solved by native bitwise operators.Comment: revised version submitted to Computer-Aided Geometric Desig
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