This page provides brief descriptions of some projects at the
McGill Mobile Robotics Lab. The projects mainly involve
aspects of environment or shape understanding using sensor data.
Ongoing Projects
Recent Projects
Optimal Spiral Search
Investigators:
Scott Burlington ,
G. Dudek
Description: Scott Burlington is doing novel research
on applying spiral search techniques to mobile robot navigation,
and multi-agent coordination. If you are stuck in a long dark
hallway and need to find the light switch as quick as you can
you should probably choose your turning points according to f(i),
where m=2 and i is the number of turns. This
scales to planar surfaces and has many synchonization issues
when applied to more than one searcher. Here is a brief summary
of some of his early work as presented to the CS
765 class.
Interest Operators
Investigators
Sandra Polifroni,
F. Ferrie, G. Dudek.
Description: Sandra Polifroni is currently doing research
on interest operators and human preattentive vision. Her work
uses both psychophysics and computer science to qualitatively
evaluate the performance of interest operators relative human
vision.
PCA Background Invariance
Investigators:
Deeptiman Jugessur,
G. Dudek.
Description: Appearance based recognition using Principal
Components Analysis with the added ability to account for varying
backgrounds. This is done using an attention operator to focus
on the object to be recognised and performing PCA only on the
sub-windows within the object.
Real-time recognition and collision
avoidance.
Investigators: Francois
Belair, Eric Bourque,
Robert Sim, I. Rekleitis, G. Dudek.
Description: Several members of the mobile robotics
group are assembling components of our software infrastructure
into a real-time mobile robotics testbed.
Computational Geometry Problems in Mobile
Robotics
Investigators: Richard
Unger, G. Dudek.
Description: Richard Unger is not yet involved in active
research, but has been working on interesting projects in his
preparatory courses. These can be found at: Robotics
Project and Computational
Geometry Project. Warning: The robotics project
applet is known to crash Navigator 4.0 on linux systems.
Distributed Robot Control Software
Environment
Investigators: G.
Dudek, Robert Sim.
Description: A Distributed, device independent mobile
robot controller and simulator. It supports distributed computation
and visualization and can control one or more real Nomad or RWI
robots. A beta version
and some additional details are available.
Defining Islands of Reliability for
Exploration and Hybrid Topological-Metric Map Construction
Investigators: S.
Simhon, G. Dudek.
Description: We are interested in the definition and
detection of landmarks and local reference frames in a large-scale
environment. We are examining automatic methods for generation
coupled navigation and sensing algorithms that are generalize
across specific sensing technologies such as vision and sonar.
These landmarks and reference frames are used to construct a
hybrid topological metric map. The representation consists of
local metric maps connected together to form a graph. Each local
map is considered a node in the graph and the edges of the graph
qualitatively describe the hierarchy and relationship of neighbouring
nodes. The work is inspired by biological environment perception.
Environment Shape and Layout from Active
Shadows
Investigators: M.
Langer (NEC), M.
Daum, G. Dudek,
S. W. Zucker
Description: This project deals with the inference of
environmental structure from shadow information. Click
here for an abstract
Multi-Robot Exploration and Rendezvous
Investigators: N.
Roy (now at CMU), I.
Rekleitis, G. Dudek.
Description: This project deals with the exploration
of an unknown environment using two or more robots working together.
Key aspects of the problems coordination, and particularly rendezvous,
between the robots, and efficient decomposition of the exploration
task.
Click here for more information on the
rendezvous work.
Object description and recognition
Investigators: G.
Dudek, Nigel Ayoung-Chee, F.
Ferrie.
Description: This project involves shape modelling based
on a combination of local curvature information at multiple scale,
and global superquadric surface fitting. Click here
for abstract.
Mobile Robot Exploration by using
Fused Data from Two Sensors
Investigators: I. Rekleitis, G. Dudek, P. Freedman.
Description: This research investigates the combined
use of a sonar range finder and a laser range finder (QUADRIS
or BIRIS) for exploring a structured indoor environment. The
methodology is called "just-in-time" sensing. More
information is available here.
Virtual Environment Construction
Investigators: Eric
Bourque, Philippe
Ciaravola, G. Dudek.
Description: We are examining techniques for the creation
and management of virtual reality analogues for the real world.
This includes the automatic acquisition of image-based VR images,
as well as the automated selection of viewpoints and scenes of
interest. Further information on the image acquisition system
is available here.
Localizing a Robot with
Minimum Travel
Investigators: G.
Dudek, Kathleen
Romanik Sue Whitesides.
Description: Click here for abstract
Accurate Position Estimation from
Learned Visual Landmarks
Investigators: Robert
Sim, G. Dudek.
Description: Methods for learning, encoding, detecting,
and using visual landmarks for mobile robot pose estimation.
Multi-Robot Collaboration
Investigators: G.
Dudek in collaboration with Professors E. Milios and M. Jenkin of York University
and D. Wilkes at Ontario Hydro.
Description: We are interested in elaborating a taxonomy
for systems of multiple mobile robots. The specific issues we
are foc using on are the relationships between inter-robot communication,
sensing, and coordination of behaviour in the context of position
estimation and exploration.
Mapping using weak information
Investigators: G.
Dudek in collaboration with Professors E. Milios and M. Jenkin of York University
and D. Wilkes at Ontario Hydro.
Description: Autonomous navigation using sensory information
often depends on a usable map of the environment. This work deals
with the automatic creation of such a maps by an autonomous agent
and the minimal requirements such a map must satisfy in order
to be useful. One aspect of this work is the analysis of how
uncertainty either in the map or in sensing devices relates to
the reliability and cost of navigation and and path planning.
Another aspect is the development of sensing strategies and behaviours
that facilitate reliable self-location and map construction.
Completed and Dormant projects
- Probabilistic sonar understanding
Simon Lacroix, Grogory Dudek
- Pose Estimation From Image Data Without Explicit Object Models
G. Dudek, Chi Zhang
We consider the problem of locating
a robot in an initially-unfamiliar environment from visual input.
The robot is not given a map of the environment, but it does
have access to a limited set of training examples each of which
specifies the video image observed when the robot is at a particular
location and orientation. Such data might be acquired using dead
reckoning the first time the robot entered an unfamiliar region
(using some simple mechanism such as sonar to avoid collisions).
In this paper, we address a specific variant of this problem
for experimental and expository purposes: how to estimate a robot's
orientation(pan and tilt) from sensor data.
Performing the requisite scene reconstruction needed to construct
a metric map of the environment using only video images is difficult.
We avoid this by using an approach in which the robot learns
to convert a set of image measurements into a representation
of its pose (position and orientation). This provides a {\em
local} metric description of the robot's relationship to a portion
of a larger environment. A large-scale map might then be constructed
from a collection of such local maps. In the case of our experiment,
these maps express the statistical relationship between the image
measurements and camera pose. The conversion from visual data
to camera pose is implemented using multi-layer neural network
that is trained using backpropagation. For extended environments,
a separate network can be trained for each local region. The
experimental data reported in this paper for orientation information
(pan and tilt) suggests the accuracy of the technique is good
while the on-line computational cost is very low.
Related work is taking place in the context of the IRIS project
(below). A recent article appears in Neural COmputation and the
abstract
is available (externally) here.
- Spatial abstraction and mapping
P. Mackenzie, G. Dudek
This project involves the development
of a formalism and methodology for making the transition from
raw noisy sensor data collected by a roving robot to a map composed
of object models and finally to a simple abstract map described
in terms of discrete places of interest. An important early stage
of such processing the the ability to select, represent and find
a discrete set of places of interest or landmarks that will make
up a map. Associated problems are those of using an map to accurately
localize a mobile robot and generating intelligent exploration
plans to verify and elaborate a map. Click
here for a compressed postscript copy of a paper on this work.
- Spatial Mapping with Uncertain Data
G. Dudek
As a sensor-based mobile robot explores an
unknown environment it collects percepts about the world it is
in. These percepts may be ambiguous individually but as a collection
they provide strong constraints on the topology of the environment.
Appropriate exploration strategies and representations allow
a limited set of possible world models to be considered as maps
of the environment. The structure of the real world and the exploration
method used specify the reliability the final map and the computational
and perceptual complexity of constructing it. Computational tools
being used to construct a map from uncertain data range from
graph-theoretic to connectionist.
- Human object recognition and shape integration
Gregory Dudek, Daniel Bub: Neurolinguistics, Montreal Neurological
Inst., Martin Arguin: Phychology Dept., University of Montreal
Computational vision is defined, to a large extent, with
reference to the visual abilities of humans. In this project
we are examining the relationship between the characteristics
of object shape and the abilities of humans to recognize these
shapes. This includes the modelling of subjects with object recognition
deficits due to brain damage as well as normal subjects. Click
here for a compressed postscript copy of a recent paper on this
work.
- Dynamic reasoning, navigation and sensing for mobile robots
IRIS Project IS-5
Martin D. Levine, Peter Caines, Renato DeMori, Gregory Dudek,
Paul Freedman (CRIM), Geoffrey Hinton (University of Toronto)
The goal of this project is to develop both the theoretical
basis and practical instantiation of a mobile robotic system
will be able to reason about tasks, recognize objects in its
environment, map its environment, understand voice commands,
and navigate through the environment and perform the specified
search tasks. This will be achieved in a dynamic environment,
in that knowledge of a (possibly changing) world may be updated,
and the tasks themselves may be radically altered during the
system's operation. Core research areas involved include perceptual
modelling, control theory, neural networks, graph theory, attentive
control of processing and speech understanding. Among the key
capabilities indended as outcomes of this project are:
- Integrated low (eg, points and lines) and high level (eg.
places and rooms) descriptions of the environment.
- Ability to deal with a changing environment.
- Ability to reason about multiple tasks and the changing environment.
- Ability to learn about the environment and the sensor characteristics.
- Ability to accept high level verbal commands (with a limited
lexicon and
syntax) similar to those employed by humans (based on psychological
data) and translate them into control actions for the robot and
sensors.
- Natural language referring expressions in a person/machine
dialogue.
G. Dudek, R. DeMori, C. Pateras
Click
here for abstract
- Reliable Vehicle Trajectory Planning
G. Dudek, Chi Zhang
We are using a hybrid method for
vehicle path planning that guarantees globally acceptable solutions
yet has limit time and space complexity. This depends on a combination
of variational methods with other approaches.
- Enhanced reality for mobile robotics
Kadima Lonji, G. Dudek
This project involves the use
of a synthetic scene model for teleoperation or pose estimation.
Live video and synthetic model information is fused to produce
a composite image.
- Multi-sensor fusion for mobile robotics
MRL group members
Click here for
abstract (with picture)
- A Flexible Behavioural Architecture for Mobile Robot Navigation
J. Zelek, M. D. Levine
The intention of this study is
to design an architecture that allows the behavioral control
strategy that is flexible, generalizable, and extendable. The
component dedicated to behavioral activities should be able to
attempt tasks with or without a reasoning module. We are investigating
2D navigational tasks for a mobile robot possessing sonar sensors
and a controllable TV camera mounted on a pan-tilt head. The
major aspects of our proposed behavioral architecture are as
follows: - A natural language lexicon is used to represent spatial
information and for defining task commands. The lexicon is used
as a language for internal communications and user-specified
commands. The task is to go to a location in space, either known
or determined by locating a specific object. - An extension of
a formalism, referred to as teleo-reactive (T-R) programs (Nilsson:94),
is used for specifying behavioral control. The extensions of
this approach involve dealing with real-time resource limitations
and constraints. Some movies of this project in action can be
viewed here:
These descriptions related to work by students who have completed
their graduate studies, or projects that are currently suspended
for some other reason.