11,023 research outputs found
Long-term experiments with an adaptive spherical view representation for navigation in changing environments
Real-world environments such as houses and offices change over time, meaning that a mobile robotās map will become out of date. In this work, we introduce a method to update the reference views in a hybrid metric-topological map so that a mobile robot can continue to localize itself in a changing environment. The updating mechanism, based on the multi-store model of human memory, incorporates a spherical metric representation of the observed visual features for each node in the map, which enables the robot to estimate its heading and navigate using multi-view geometry, as well as representing the local 3D geometry of the environment. A series of experiments demonstrate the persistence performance of the proposed system in real changing environments, including analysis of the long-term stability
Topomap: Topological Mapping and Navigation Based on Visual SLAM Maps
Visual robot navigation within large-scale, semi-structured environments
deals with various challenges such as computation intensive path planning
algorithms or insufficient knowledge about traversable spaces. Moreover, many
state-of-the-art navigation approaches only operate locally instead of gaining
a more conceptual understanding of the planning objective. This limits the
complexity of tasks a robot can accomplish and makes it harder to deal with
uncertainties that are present in the context of real-time robotics
applications. In this work, we present Topomap, a framework which simplifies
the navigation task by providing a map to the robot which is tailored for path
planning use. This novel approach transforms a sparse feature-based map from a
visual Simultaneous Localization And Mapping (SLAM) system into a
three-dimensional topological map. This is done in two steps. First, we extract
occupancy information directly from the noisy sparse point cloud. Then, we
create a set of convex free-space clusters, which are the vertices of the
topological map. We show that this representation improves the efficiency of
global planning, and we provide a complete derivation of our algorithm.
Planning experiments on real world datasets demonstrate that we achieve similar
performance as RRT* with significantly lower computation times and storage
requirements. Finally, we test our algorithm on a mobile robotic platform to
prove its advantages.Comment: 8 page
A reconfigurable hybrid intelligent system for robot navigation
Soft computing has come of age to o er us a wide array of powerful and e cient algorithms
that independently matured and in
uenced our approach to solving problems in robotics,
search and optimisation. The steady progress of technology, however, induced a
ux of new
real-world applications that demand for more robust and adaptive computational paradigms,
tailored speci cally for the problem domain. This gave rise to hybrid intelligent systems, and
to name a few of the successful ones, we have the integration of fuzzy logic, genetic algorithms
and neural networks. As noted in the literature, they are signi cantly more powerful than
individual algorithms, and therefore have been the subject of research activities in the past
decades. There are problems, however, that have not succumbed to traditional hybridisation
approaches, pushing the limits of current intelligent systems design, questioning their solutions
of a guarantee of optimality, real-time execution and self-calibration. This work presents an
improved hybrid solution to the problem of integrated dynamic target pursuit and obstacle
avoidance, comprising of a cascade of fuzzy logic systems, genetic algorithm, the A* search
algorithm and the Voronoi diagram generation algorithm
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
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
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