Vision-based robotics: Representation, Mapping and Exploration
Speaker: Robert Sim , , University of British Columbia
Date: November 22 2005
Time: 4:00PM to 5:00PM
Location: 32-G463
Host: Daniela Rus, MIT
Contact: Alise, 253-2773, alise@csail.mit.edu
Relevant URL: Autonomous mobile robot systems have an important role to play in a
wide variety of application domains. A key component for autonomy is
the capability to explore an unknown environment and construct a
representation that a robotic agent can use to localize, navigate, and
reason about the world. In this talk I will present results on the
automatic construction of visual representations. First, the Visual
Map representation will be introduced as a method for modelling the
visual structure of the world. Second, I will present a flexible
architecture for robust real-time vision-based mapping of an unknown
environment. Finally, I will conclude with a discussion of recent
progress on the problem of autonomous robotic exploration, and
illustrate issues in the problem of developing robotic explorers that
are naturally curious about their environment.
The Visual Map framework is an approach to representing the visual
world that enables a robot to learn models of the salient visual
features of an environment. A key component of this representation is
the ability to learn mappings between camera pose and image-domain
features without imposing a priori assumptions about the structure of
the environment, or the optical characteristics of the visual sensor.
These mappings can be employed as generative models in a Bayesian
framework for solving the robot localization problem, as well as for
visual servoing and path planning.
The second part of this talk demonstrates an architecture for
performing simultaneous localization and mapping with vision. The main
goals of our work are to facilitate robust large-scale mapping in real
time using vision. We employ a Rao-Blackwellised particle filter for
managing uncertainty and examine a variety of robust proposal
distributions, as well as the run-time and scaling characteristics of
our architecture.
The latter part of this builds on representation and mapping to
address robotic exploration. In order to acquire a representation of
the world, a robot must first acquire data. From an
information-theoretic point of view, this problem involves moving
through the world so as to maximize the information that can be gained
from what is observed along the robot's trajectory. However,
computing the optimal trajectory is complicated by several factors,
including the presence of noise, the time horizon over which the robot
plans, the specific objective function that is optimized, and the
robot's choice of sensor. I will present several results in this area
that lead to the development of robust robotic systems that can plan
over the long term and successfully demonstrate an emergent sense of
curiosity.
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