1,111 research outputs found
Spatial Aggregation: Theory and Applications
Visual thinking plays an important role in scientific reasoning. Based on the
research in automating diverse reasoning tasks about dynamical systems,
nonlinear controllers, kinematic mechanisms, and fluid motion, we have
identified a style of visual thinking, imagistic reasoning. Imagistic reasoning
organizes computations around image-like, analogue representations so that
perceptual and symbolic operations can be brought to bear to infer structure
and behavior. Programs incorporating imagistic reasoning have been shown to
perform at an expert level in domains that defy current analytic or numerical
methods. We have developed a computational paradigm, spatial aggregation, to
unify the description of a class of imagistic problem solvers. A program
written in this paradigm has the following properties. It takes a continuous
field and optional objective functions as input, and produces high-level
descriptions of structure, behavior, or control actions. It computes a
multi-layer of intermediate representations, called spatial aggregates, by
forming equivalence classes and adjacency relations. It employs a small set of
generic operators such as aggregation, classification, and localization to
perform bidirectional mapping between the information-rich field and
successively more abstract spatial aggregates. It uses a data structure, the
neighborhood graph, as a common interface to modularize computations. To
illustrate our theory, we describe the computational structure of three
implemented problem solvers -- KAM, MAPS, and HIPAIR --- in terms of the
spatial aggregation generic operators by mixing and matching a library of
commonly used routines.Comment: See http://www.jair.org/ for any accompanying file
Monte Carlo Localization in Hand-Drawn Maps
Robot localization is a one of the most important problems in robotics. Most
of the existing approaches assume that the map of the environment is available
beforehand and focus on accurate metrical localization. In this paper, we
address the localization problem when the map of the environment is not present
beforehand, and the robot relies on a hand-drawn map from a non-expert user. We
addressed this problem by expressing the robot pose in the pixel coordinate and
simultaneously estimate a local deformation of the hand-drawn map. Experiments
show that we are able to localize the robot in the correct room with a
robustness up to 80
New conserved structural fields for supercooled liquids
By considering Voronoi tessellations of the configurations of a fluid, we
propose two new conserved fields, which provide structural information not
fully accounted for by the usual 2-point density field fluctuations (structure
factor). One of these fields is scalar and associated to the Voronoi cell
volumes, whereas the other one, termed the "geometrical polarisation", is
vectorial, related to the very local anisotropy of the configurations. We study
the static and dynamical properties of these fields in the supercooled regime
of a model glass-forming liquid. We show in particular that the geometrical
polarisation is both statically correlated to the force field and contrary to
it develops a plateau regime when the temperature is lowered. We attribute this
behaviour to the microsopic disorder of the underlying inherent structures (IS)
which dictate the dynamics on time scales larger than the true microscopic
time, in the strong supercooled regime. In this respect, this work raises the
issue of to what extent the inter IS dynamics, intrinsically anisotropic and
collective (cf. T.B. Schr{\o}der et al. {\it J. of Chem. Phys.}, {\bf 112},
9834 (2000)), could be related to their polarisation field.Comment: submitted to EPJE the 09/30/201
Emergence of diverse collective behaviors from local topological perception
We present a dynamical model for generic crowds in 2D in which individual
agents are aware of their local environment -- i.e., neighboring agents and
domain boundary features -- and may seek static target locations. Our model
incorporates features common to many other "active matter" models like
collision avoidance, alignment among agents, and homing toward targets.
However, it is novel in key respects: the model combines topological and
metrical features in a natural manner based upon the local environment of the
agent's Voronoi diagram. With only two parameters, it is shown to capture a
wide range of collective behaviors.Comment: 15 pages, 6 figures, Gonzalez and Tisdell contributed equall
Dynamic Detection of Topological Information from Grid-Based Generalized Voronoi Diagrams
In the context of robotics, the grid-based Generalized Voronoi Diagrams (GVDs) are widely used by mobile robots to represent their surrounding area. Current approaches for incrementally constructing GVDs mainly focus on providing metric skeletons of underlying grids, while the connectivity among GVD vertices and edges remains implicit, which makes high-level spatial reasoning tasks impractical. In this paper, we present an algorithm named Dynamic Topology Detector (DTD) for extracting a GVD with topological information from a grid map. Beyond the construction and reconstruction of a GVD on grids, DTD further extracts connectivity among the GVD edges and vertices. DTD also provides efficient repair mechanism to treat with local changes, making it work well in dynamic environments. Simulation tests in representative scenarios demonstrate that (1) compared with the static algorithms, DTD generally makes an order of magnitude improvement regarding computation times when working in dynamic environments; (2) with negligible extra computation, DTD detects topologies not computed by existing incremental algorithms. We also demonstrate the usefulness of the resulting topological information for high-level path planning tasks
Conceptual spatial representations for indoor mobile robots
We present an approach for creating conceptual representations of human-made indoor environments using mobile
robots. The concepts refer to spatial and functional properties of typical indoor environments. Following ļ¬ndings
in cognitive psychology, our model is composed of layers representing maps at diļ¬erent levels of abstraction. The
complete system is integrated in a mobile robot endowed with laser and vision sensors for place and object recognition.
The system also incorporates a linguistic framework that actively supports the map acquisition process, and which
is used for situated dialogue. Finally, we discuss the capabilities of the integrated system
Autonomous Navigation and Mapping using Monocular Low-Resolution Grayscale Vision
Vision has been a powerful tool for navigation of intelligent and man-made systems ever since the cybernetics revolution in the 1970s. There have been two basic approaches to the navigation of computer controlled systems: The self-contained bottom-up development of sensorimotor abilities, namely perception and mobility, and the top-down approach, namely artificial intelligence, reasoning and knowledge based methods. The three-fold goal of autonomous exploration, mapping and localization of a mobile robot however, needs to be developed within a single framework. An algorithm is proposed to answer the challenges of autonomous corridor navigation and mapping by a mobile robot equipped with a single forward-facing camera. Using a combination of corridor ceiling lights, visual homing, and entropy, the robot is able to perform straight line navigation down the center of an unknown corridor. Turning at the end of a corridor is accomplished using Jeffrey divergence and time-to-collision, while deflection from dead ends and blank walls uses a scalar entropy measure of the entire image. When combined, these metrics allow the robot to navigate in both textured and untextured environments. The robot can autonomously explore an unknown indoor environment, recovering from difficult situations like corners, blank walls, and initial heading toward a wall. While exploring, the algorithm constructs a Voronoi-based topo-geometric map with nodes representing distinctive places like doors, water fountains, and other corridors. Because the algorithm is based entirely upon low-resolution (32 x 24) grayscale images, processing occurs at over 1000 frames per second
Semi-Global Stereo Matching with Surface Orientation Priors
Semi-Global Matching (SGM) is a widely-used efficient stereo matching
technique. It works well for textured scenes, but fails on untextured slanted
surfaces due to its fronto-parallel smoothness assumption. To remedy this
problem, we propose a simple extension, termed SGM-P, to utilize precomputed
surface orientation priors. Such priors favor different surface slants in
different 2D image regions or 3D scene regions and can be derived in various
ways. In this paper we evaluate plane orientation priors derived from stereo
matching at a coarser resolution and show that such priors can yield
significant performance gains for difficult weakly-textured scenes. We also
explore surface normal priors derived from Manhattan-world assumptions, and we
analyze the potential performance gains using oracle priors derived from
ground-truth data. SGM-P only adds a minor computational overhead to SGM and is
an attractive alternative to more complex methods employing higher-order
smoothness terms.Comment: extended draft of 3DV 2017 (spotlight) pape
- ā¦