Exploiting structure in man-made environments
Speaker: Alper Aydemir , KTH - Computer Vision and Active Perception Lab
Date: April 10 2012
Time: 1:00PM to 2:00PM
Location: 32-D463 (Star Seminar Room)
Host: Seth Teller, MIT : CSAIL : Robotics, Vision, Sensor Networks Gr
Contact: Britton 'Bryt' Bradley, 617-253-6583, bryt@csail.mit.edu
We will present three strands of work in this talk on exploiting the
structure of man-made environments at both large scale (buildings)
and small scale (scenes).
Analysis and prediction of the large scale structure of indoor
environments
----------------------------------------------------------------------
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 datasets.
Learning 3D context of everyday objects
----------------------------------------------------------------------
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
----------------------------------------------------------------------
We present Kinect@Home (http://kinectathome.com), in collaboration
with the 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.
See other events that are part of
See other events happening in April 2012
