Foundations of Robotics Seminar, April 12, 2012
Time
and Place | Seminar Abstract
Exploiting structure in man-made environments
Alper Aydemir
Computer Vision and Active Perception Lab
KTH
Thursday, April 12, 2012
NSH 1507
Talk 4:30 pm
In this talk we will present three strands of work on exploiting
the structure in man-made environments both in large scale (buildings)
and small scale (scenes).
What can we learn from 38,000 rooms?
Reasoning about unexplored space in indoor environments
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Various robotics tasks ranging from exploration to fetch-and-carry
missions in partially known environments require the robot to predict
what lies in the unexplored part of the environment. In this talk we
first analyze a large set of indoor environments, namely from two
large annotated floor plan data sets corresponding to the buildings
from the MIT and KTH campuses. Utilizing tools
from graph theory we provide certain characteristics that emerge from
real-world indoor environments. Following this
analysis, we propose two methods for predicting both the topology and
the categories of rooms given a partial map. We provide extensive
experimental results that evaluate their performance. In particular,
we analyze the transferability of our models between the two data
sets.
Exploiting and modeling local 3D structure for predicting object locations
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Previous work has shown that contextual cues can greatly help in
locating and identifying objects. In this talk, we argue that there
is a strong correlation between local 3D structure and object
placement in everyday scenes. We call this the 3D context of the
object. We present one method to capture the 3D context of different
object classes. For evaluation, we have collected a large dataset of
Microsoft Kinect frames from five different locations in Europe, which
we also make publicly available. We provide extensive experiments that
show the plausibility of the 3D context idea and our realization. Our
experimental results support that the 3D structure surrounding objects
in everyday scenes is a strong indicator of their placement.
Kinect@Home: Crowdsourcing a Large 3D Dataset of Real Environments
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We present Kinect@Home (http://kinectathome.com), in collaboration
with MIT Media Lab, aimed at
collecting a vast RGB-D dataset from real everyday living spaces. This
dataset is planned to be the largest
real world image collection of everyday environments to date, making
use of the availability of a widely adopted robotics sensor which is
also in the homes of millions of users, the Microsoft Kinect camera.
Alper Aydemir is a PhD student at CVAP, KTH working on developing
methods to efficiently search for objects in large-scale environments.
He works with Assoc. Prof. Patric Jensfelt on the EU project CogX.
Previously, he worked as an research intern at the Carnegie Mellon
University, Robotics Institute.
He holds a BSc on Mechatronics Engineering, Sabanci University, Turkey.
The Robotics Institute is part of the School of Computer Science, Carnegie Mellon University.
Website: http://csc.kth.se/~aydemir