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Robot
homing based on panoramic vision |
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Brief description |
Taking
into account the fact that vision and perception is not the goal but the
means, the focus is shifted from studying what an artificial system can
perceive through vision, to studying what an artificial system really needs
to perceive in order to fulfill its goals. This shift of focus constitutes is
important because perceptual processes are not studied in isolation but in
the context of the goals, the environment and the behaviors of an artificial
system. In this framework we propose a novel, vision-based method for robot homing, the
problem of computing a route so that a robot can return to its initial “home”
position after the execution of an arbitrary “prior” path. The method assumes
that the robot tracks visual features in panoramic views of the environment
that it acquires as it moves. By exploiting only angular information
regarding the tracked features, a local control strategy moves the robot
between two positions, provided that there are at least three features that
can be matched in the panoramas acquired at these positions. The strategy is
successful when certain geometric constraints on the configuration of the two
positions relative to the features are fulfilled. In order to achieve
long-range homing, the features’ trajectories are organized in a visual
memory during the execution of the “prior” path. When homing is initiated,
the robot selects Milestone Positions (MPs) on the “prior” path by exploiting
information in its visual memory. The MP selection process aims at picking
positions that guarantee the success of the local control strategy between
two consecutive MPs. The sequence of successive MPs successfully guides the
robot even if the visual context in the “home” position is radically
different from the visual context at the position where homing was initiated.
Experimental results from a prototype implementation of the method
demonstrate that homing can be achieved with high accuracy, independent of
the distance traveled by the robot. The contribution of this work is that it
shows how a complex navigational task such as homing can be accomplished
efficiently, robustly and in real-time by exploiting primitive visual cues. Such
cues carry implicit information regarding the 3D structure of the
environment. Thus, the computation of explicit range information and the
existence of a geometric map are not required. |
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An illustration of the robot
homing problem. The robot starts at position A and moves until it reaches
some position T. The problem is then to return back to its original home position
A. |
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Contributions |
Antonis
Argyros, Kostas
Bekris, Stelios Orphanoudakis,
Lydia Kavraki. |
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Sample results |
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The method employs a local
control strategy that permits the robot to move between two adjacent
positions with the aid of angular information regarding at least three
landmarks that need to be identified and corresponded between the two views.
More information on this control strategy can be found in the related papers
(see at the bottom of the page). It turns out that by employing this control
strategy, the robot can move between two advacent positions, although not
necessarily on a straight line. The control strategy can be generalized for
more than one landmarks. |
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The properties of the proposed
control strategy have been studied and its reachability area has been
identified. In the above figure, you may see with gray color the reachability
area of the control strategy for the cases of three and five landmarks,
respectively. This means that the robot can reach any gray point on the
plane, regardless of the starting position, by employing the proposed control
strategy. Note that the convex hull of landmarks is always reachable. |
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Our approach to homing exploits
the proposed local control strategy in order to build a visual memory. This
way, the robot solves the homing problem by employing the proposed local
control law between automatically defined milestone positions. The required
feature matching is achieved through KLT corner tracking (in the examples
seen below) or by employing the home-built
color tracker. More related results can be found on the more detailed
page on angle-based
robot navigation. |
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The above layout show
approximately the layout of the space where a homing experiment has been
conducted. |
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In the above video you may see
a video from the homing experiment. The circular mark on the floor is used to
indicate the accuracy of the proposed homing method in reaching the home
position. |
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In the above video you may see
the KLT tracking of corners during the homing above homing experiment. |
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Related publications
and documents |
·
A.A. Argyros, C. Bekris, S.C.
Orphanoudakis, L.E. Kavraki, “Robot Homing by Exploiting Panoramic Vision”,
Journal of Autonomous Robots, vol. 19, no. 1, pp. 7-25, July 2005. ·
K. E. Bekris, A.A. Argyros, L. E.
Kavraki, “Angle-Based Methods for Mobile Robot Navigation”, Lecture Notes in
Computer Science, “Imaging beyond the Pinhole Camera”, K. Daniilidis, R.
Kleete, (editors), in press. ·
K. E. Bekris, A.A. Argyros, L. E.
Kavraki, “Angle-Based Methods for Mobile Robot Navigation: Reaching the
Entire Plane”, in proceedings of the International Conference on Robotics and
Automation (ICRA‘04), pp. 2373-2378, New Orleans, USA, April 26 - May 1st,
2004. ·
A.A. Argyros, C. Bekris, S. Orphanoudakis,
“Robot Homing based on Corner Tracking in a Sequence of Panoramic Images”, in
proceedings of the Computer Vision and Pattern Recognition Conference (CVPR‘01), pp. 3-10, Hawaii, USA,
December 11-14, 2001. The electronic versions of the above publications can be downloaded
from my publications
page. |
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Last update |
24 May 2005, Antonis Argyros, argyros@ics.forth.gr |
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