The Robotics Institute

RI | Centers | CFR | Seminar

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



Time and Place

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
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
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 (, 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.