Session WedAT1 Pegaso A Wednesday, October 10, 2012 ... - Lirmm

Session WedET6 Gemini 3 Wednesday, October 10, 2012, 15:00–16:00
Mapping IV
Chair
Co-Chair
15:00–15:15 WedET6.1
What can we learn from 38,000 rooms?
Reasoning about unexplored space in indoor
environments
Alper Aydemir, Patric Jensfelt and John Folkesson
Center for Autonomous Systems, KTH, Sweden
• Reasoning about unexplored space is a
key part of spatial understanding
• We report statistical properties of indoor
environments by investigating the two floor
plan data sets from different parts of the
SHAFT
world, namely the KTH and MIT datasets.
• We present two methods for predicting
indoor topologies given a partial map of
the environment.
• We make the KTH campus data set, our
annotation tool and software library
developed during this work publicly
available at
http://www.cas.kth.se/floorplans
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Topological representation
of a building floor
15:30–15:45 WedET6.3
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Creating and Using Probabilistic Costmaps from
Vehicle Experience
Liz Murphy, Steven Martin and Peter Corke
CyPhy Lab, Queensland University of Technology, Australia
• Probabilistic costmaps, unlike the
predominant assumptive costmaps, allow
a representation of the uncertainty in the
robot's environment model to be used in
path planning
• We show how probabilistic costmaps can
be learned in a self-supervised manner by
robots navigating outdoors
• Traversability estimates are garnered from
onboard sensing
• Gaussian processes are used to
extrapolate sparse these sparse
traversability estimates and account for
heteroscedastic noise
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15:15–15:30 WedET6.2
Map Merging Using Hough Peak Matching
Sajad Saeedi ♦ , Liam Paull ♦ , Michael Trentini ♦♦ , Mae Seto ♦♦ and
Howard Li ♦
♦ Electrical and Computer Engineering, University of New Brunswick, Canada
♦♦ Defence Research and Development Canada, Canada
• One of the major problems for multi-robot
SLAM is that the robots only know their
positions in their own local coordinate
frames, so fusing map data can be
challenging.
• In this research, map fusion is achieved by
transforming individual maps into the
Hough space where they are represented
in an abstract form.
• Properties of the Hough transform are
used to find the common regions in the
maps, which are then used to calculate
the unknown transformation between the
maps.
2012 IEEE/RSJ International Conference on Intelligent Robots and Systems
–166–
Three partial maps (a, b and c)
are fused to generate a global
map (d) using Hough peak
matching
15:45–16:00 WedET6.4
Dynamic Visual Understanding of the Local
Environment for an Indoor Navigating Robot
Grace Tsai and Benjamin Kuipers
Electrical Engineering and Computer Science,
University of Michigan, Ann Arbor
• Represent indoor environment by
a set of meaningful planes –
ground and walls.
• Generate qualitatively distinct 3D
structural hypotheses from image
features incrementally.
• Evaluate a set of qualitatively
distinct hypotheses through a
Bayesian filter while refining the
quantitative precision of each
hypothesis based on current
observations.
• Runs in real-time, without the
need for prior training data or the
Manhattan-world assumption.