[robotics-worldwide] [meetings] Reminder [deadline May 16] - Call for abstracts and live demos - RSS 2014 Workshop on Resource-efficient Integration of Planning and Perception for true autonomous operation of Micro Air Vehicles (MAVs)

Darius Burschka burschka at cs.tum.edu
Mon May 12 01:00:21 PDT 2014

The deadline for the RSS 2014 workshop on Resource-efficient Integration
of Planning and Perception for true autonomous operation of Micro Air
Vehicles (MAVs) is approaching.



RSS 2014 Workshop on Resource-efficient Integration of Planning and
Perception for true autonomous operation of Micro Air Vehicles (MAVs),
July 12th, 2014 


Berkeley, California



Extended abstracts deadline:   May 16th, 2014  
Notification of Acceptance:    May 31st, 2014
Final submission:              June 30th, 2014
Workshop at RSS 2014:          July 12, 2014


The emergence of power efficient Embedded Systems together with the
availability of cheap flying platforms leads to an increased demand for
robust navigation systems. While the inherent instability of these
platforms requires an integration of inertial sensors for control,
fusion of further exteroceptive sensor data is necessary for high-level
navigation. MAVs must operate autonomously in GPS and radio link denied
environments, requiring resource-efficient algorithms running on-board 

The research on fusion algorithms for robust navigation on flying
systems in GPS-denied environments has made great progress and stable
approaches are available now. The efficient sensor data processing on
resource-limited systems with e.g. FPGA based image processing enabled
novel perception systems achieving already good maturity levels.
However, the environment representations for flying systems such as
visual maps, topological maps, sparse and dense models require more
research efforts. Furthermore, the methods for online 3D planning and
obstacle avoidance and their integration with perception and navigation
are still at an early stage. To achieve true autonomy of MAVs, these
topics need consideration and will be the main focus of the workshop.

After the great success of the RSS workshops in 2012 and 2013, we aim to
again bring together researchers working on sensor data processing and
fusion for robust navigation of flying platforms with focus on
autonomous operation. The goal is to provide an opportunity to compare
and discuss the current state-of-the-art frameworks on flying robots as
well as different approaches for navigation and data fusion based on
exact maps, topological information, and visual maps with focus on
autonomy of the systems. Live demonstrations are intended and will be

The workshop aims to spark discussion with talks from invited speakers,
presentations from authors of accepted papers, and a live demo session.
We are also interested in lessons learned and documentations of failures
so that the community can benefit from this experience.

We are soliciting two types of contributions:

- Extended abstracts (2 pages) for oral presentation
- Extended abstracts (2 pages) for LIVE demo presentations

Submissions should be submitted via eMail using the following address:
rss-workshop-perception at dlr.de

The workshop proposal is based on two very successful workshops on the
RSS 2012 and 2013. The audience intended is the flying robots, AI and
autonomous systems community


Nicholas Roy (MIT)
Jürgen Sturm (TUM)
Davide Scaramuzza (University of Zurich)


Darius Burschka 
TU Munich - Machine Vision and Perception Group
burschka at cs.tum.edu

Michael Suppa
DLR – Institute of Robotics and Mechatronics
Michael.Suppa at dlr.de

Roland Siegwart
ETH Zürich - Autonomous Systems Lab
rsiegwart at ethz.ch

Korbinian Schmid
DLR - Institute of Robotics and Mechatronics
Korbinian.Schmid at dlr.de

Markus Achtelik
ETH Zürich - Autonomous Systems Lab
markus.achtelik at mavt.ethz.ch

I like the whooshing sound of the deadlines passing by...

Prof. Dr.-Ing. Darius Burschka  Associate Professor
Phone: (+49 89) 289-17638       Department of Informatics
Fax:   (+49 89) 411-71603       Telerobotics and Sensor-Data Fusion
http://www6.in.tum.de/burschka/ Technische Universität München

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