Transcript Robotica autonoma

Informatica e Robotica per
l’Automazione (IRA) Lab.
Informatica e Robotica per l’Automazione (IRA) Lab.
Dipartimento Informatica, Sistemistica e Comunicazione,
Università degli Studi di Milano – Bicocca
Domenico G. Sorrenti
[email protected]
http://www.disco.unimib.it/sorrenti
lab. website: http://www.ira.disco.unimib.it
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Research activities
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In the IRAlab we pursue research in autonomous
systems;
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Most of our work concerns autonomous robots;
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What are autonomous robots? Let’s see 3 examples.
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Autonomous robots example 1
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Autonomous robots example 2
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Autonomous robots example 3
QuickTime™ and a
decompressor
are needed to see this picture.
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Autonomous robots example 4
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Autonomous robots example 4
QuickTime™ and a
YUV420 codec decompressor
are needed to see this picture.
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So, what are
autonomous robots?
A machine that receives information from its working environment by
means of its sensors, and basing on this information decides
autonomously what action to execute, out of the ones available, in
order to pursue a given task.
Percezione
Decisione
Perception
Perception
Decision
Decision
Action
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Azione
Action
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Autonomous robots
as controllers
modeling of physics
complex modeling of physics
probabilistic reasoning
control systems
programming
programming
analog and digital electronics
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Staff
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Domenico G. Sorrenti, assoc. prof.
Daniele Marzorati, research assoc.
Axel Furlan, research assist.
students
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Main research topics
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Vision-based SLAM (Simultaneous Localization and
Mapping);
Multi-target tracking;
Benchmarking, a cultural approach, orthogonal to the
topics.
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Vision-based SLAM
costruzione di un
modello (mappa)
dell’ambiente di
lavoro
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Vision-based SLAM
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we tackled the uncertainty modeling problem, relevant in visionbased SLAM;
we are now at the frontier of the research, at the international level,
with the “inverse scaling” parameterization;
expectations:
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to consolidate the results publishing comparisons with other proposals
on known benchmarks;
to handle large maps (CISLAM approach from Zaragoza);
to handle dynamic scenes;
this is a joint work with Politecnico di Milano;
cooperation: Universidad de Zaragoza, Albert Ludwig Universiteit
Freiburg;
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Multi-target tracking
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Multi-target tracking
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we tackled the problem of handling ambiguities in tracking, relevant
in vision-based navigation (indoor and outdoor);
in this field we are now behind the frontier of the research, at the
international level;
expectations:
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to publish our current results in papers based on comparisons with other
proposals, on known benchmarks;
to integrate with the research in SLAM o increase accuracy in both
systems;
to apply our systems to both indoor (project Grandi Attrezzature 2008)
and outdoor (robotic cars/buses) navigation.
cooperation: Politecnico di Milano
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Benchmarking in SLAM
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Benchmarking in SLAM
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we tackled the problem of procuring datasets for benchmarking of SLAM algorithm by
any research groups, in a project funded by the EEC, quite successful;
this is a “support of the research” activity, though innovation had to be brought in for:
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in this field we are the frontier of the research, at the international level;
expectations:
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the definition of the Ground Truth collection systems for the robot pose,
the definition of performance measures,
the definition of some algorithms, i.e., benchmark solutions;
to publish our results in papers devoted to robotic benchmarking;
to apply our SLAM research results to the collected datasets;
to maintain the lead in the field, e.g., by using the Grandi Attrezzature 2008 infrastructure for
benchmarking research fields where offline datasets are not viable.
cooperation: Politecnico di Milano, Universidad de Zaragoza, Albert Ludwig
Universiteit Freiburg;
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Why robotics is
different?
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Taxonomy of research activity:
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at the conceptual level: pure pencil and paper work;
at the software implementation level: software development;
at the hardware level: robotics, and other “real” research activities.
Real example:
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during the “Sant’Ambrogio” week in December 2008 about 10 people from UNIMIB and
POLIMI spent the something more than one week, after preparing for this activity, collecting
indoor datasets for SLAM benchmarking in the Bicocca location.
about 8 top-qualified people from UNIZR and ALUFR spent at about the same amount for
checking and validating the datasets.
End of January it turned out that a bug in a newer version of the IEE1394 (Firewire) driver
invalidated all such datasets.
The datasets had to be re-collected; it took about one month of full pipelined work for the
same number of people, which amounts to more than one year of labor.
Pure pencil and paper research do not risk such troubles, and the same applies to pure
software research.
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Conclusions
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Both SLAM and multi-target tracking are required for
autonomous driving, in any application: robotized
car/buses, indoor autonomous navigation services,
unmanned ground / air / underwater vehicles.
In a few years many robotic product will reach the
market; shall we be ready for selling them or we will be
just customers?
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