| 1. |
- Aydemir, Alper, et al.
(författare)
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Active Visual Object Search in Unknown Environments Using Uncertain Semantics
- 2013
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Ingår i: IEEE Transactions on robotics. - 1552-3098 .- 1941-0468. ; 29:4, s. 986-1002
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we study the problem of active visual search (AVS) in large, unknown, or partially known environments. We argue that by making use of uncertain semantics of the environment, a robot tasked with finding an object can devise efficient search strategies that can locate everyday objects at the scale of an entire building floor, which is previously unknown to the robot. To realize this, we present a probabilistic model of the search environment, which allows for prioritizing the search effort to those parts of the environment that are most promising for a specific object type. Further, we describe a method for reasoning about the unexplored part of the environment for goal-directed exploration with the purpose of object search. We demonstrate the validity of our approach by comparing it with two other search systems in terms of search trajectory length and time. First, we implement a greedy coverage-based search strategy that is found in previous work. Second, we let human participants search for objects as an alternative comparison for our method. Our results show that AVS strategies that exploit uncertain semantics of the environment are a very promising idea, and our method pushes the state-of-the-art forward in AVS.
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| 2. |
- Aydemir, Alper, et al.
(författare)
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Exploiting and modeling local 3D structure for predicting object locations
- 2012
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Ingår i: Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on. - : IEEE. - 9781467317375 ; , s. 3885-3892
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Konferensbidrag (refereegranskat)abstract
- In this paper, 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. In previous work, this is typically hand-coded and limited to flat horizontal surfaces. In contrast, we propose to use a more general model for 3D context and learn the relationship between 3D context and different object classes. This way, we can capture more complex 3D contexts without implementing specialized routines. We present extensive experiments with both qualitative and quantitative evaluations of our method for different object classes. We show that our method can be used in conjunction with an object detection algorithm to reduce the rate of false positives. Our results support that the 3D structure surrounding objects in everyday scenes is a strong indicator of their placement and that it can give significant improvements in the performance of, for example, an object detection system. For evaluation, we have collected a large dataset of Microsoft Kinect frames from five different locations, which we also make publicly available.
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| 3. |
- Aydemir, Alper, 1984-
(författare)
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Exploiting structure in man-made environments
- 2012
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Robots are envisioned to take on jobs that are dirty, dangerous and dull, the three D's of robotics. With this mission, robotic technology today is ubiquitous on the factory floor. However, the same level of success has not occurred when it comes to robots that operate in everyday living spaces, such as homes and offices.A big part of this is attributed to domestic environments being complex and unstructured as opposed to factory settings which can be set up and precisely known in advance. In this thesis we challenge the point of view which regards man-made environments as unstructured and that robots should operate without prior assumptions about the world. Instead, we argue that robots should make use of the inherent structure of everyday living spaces across various scales and applications, in the form of contextual and prior information, and that doing so can improve the performance of robotic tasks.To investigate this premise, we start by attempting to solve a hard and realistic problem, active visual search. The particular scenario considered is that of a mobile robot tasked with finding an object on an entire unexplored building floor. We show that a search strategy which exploits the structure of indoor environments offers significant improvements on state of the art and is comparable to humans in terms of search performance. Based on the work on active visual search, we present two specific ways of making use of the structure of space. First, we propose to use the local 3D geometry as a strong indicator of objects in indoor scenes. By learning a 3D context model for various object categories, we demonstrate a method that can reliably predict the location of objects. Second, we turn our attention to predicting what lies in the unexplored part of the environment at the scale of rooms and building floors. By analyzing a large dataset, we propose that indoor environments can be thought of as being composed out of frequently occurring functional subparts. Utilizing these, we present a method that can make informed predictions about the unknown part of a given indoor environment.The ideas presented in this thesis explore various sides of the same idea: modeling and exploiting the structure inherent in indoor environments for the sake of improving robot's performance on various applications. We believe that in addition to contributing some answers, the work presented in this thesis will generate additional, fruitful questions.
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| 4. |
- Aydemir, Alper, et al.
(författare)
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Kinect@Home : Crowdsourcing a large 3D dataset of real environments
- 2012
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Ingår i: AAAI Spring Symposium - Technical Report. - 9781577355557 ; , s. 8-9
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Konferensbidrag (refereegranskat)abstract
- We present Kinect@Home, 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.
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| 5. |
- Aydemir, Alper, et al.
(författare)
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Object search on a mobile robot using relational spatial information
- 2010
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Ingår i: Proc. of the 11th Int Conference on Intelligent Autonomous Systems (IAS-11). - Amsterdam : IOS Press. - 9781607506126 ; , s. 111-120
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Konferensbidrag (refereegranskat)abstract
- We present a method for utilising knowledge of qualitative spatial relations between objects in order to facilitate efficient visual search for those objects. A computational model for the relation is used to sample a probability distribution that guides the selection of camera views. Specifically we examine the spatial relation “on”, in the sense of physical support, and show its usefulness in search experiments on a real robot. We also experimentally compare different search strategies and verify the efficiency of so-called indirect search.
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| 6. |
- Aydemir, Alper, et al.
(författare)
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Plan-based Object Search and Exploration Using Semantic Spatial Knowledge in the Real World
- 2011
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Ingår i: Proc. of the European Conference on Mobile Robotics (ECMR'11).
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Konferensbidrag (refereegranskat)abstract
- In this paper we present a principled planner based approach to the active visual object search problem in unknown environments. We make use of a hierarchical planner that combines the strength of decision theory and heuristics. Furthermore, our object search approach leverages on the conceptual spatial knowledge in the form of object cooccurences and semantic place categorisation. A hierarchical model for representing object locations is presented with which the planner is able to perform indirect search. Finally we present real world experiments to show the feasibility of the approach.
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| 7. |
- Aydemir, Alper, et al.
(författare)
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Predicting what lies ahead in the topology of indoor environments
- 2012
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Ingår i: Spatial Cognition VIII. - Berlin, Heidelberg : Springer. - 9783642327315 ; , s. 1-16
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Konferensbidrag (refereegranskat)abstract
- A significant amount of research in robotics is aimed towards building robots that operate indoors yet there exists little analysis of how human spaces are organized. In this work we analyze the properties of indoor environments from a large annotated floorplan dataset. We analyze a corpus of 567 floors, 6426 spaces with 91 room types and 8446 connections between rooms corresponding to real places. We present a system that, given a partial graph, predicts the rest of the topology by building a model from this dataset. Our hypothesis is that indoor topologies consists of multiple smaller functional parts. We demonstrate the applicability of our approach with experimental results. We expect that our analysis paves the way for more data driven research on indoor environments.
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| 8. |
- Aydemir, Alper, et al.
(författare)
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Search in the real world : Active visual object search based on spatial relations
- 2011
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Ingår i: IEEE International Conference on Robotics and Automation (ICRA), 2011. - : IEEE. - 9781612843865 ; , s. 2818-2824
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Konferensbidrag (refereegranskat)abstract
- Objects are integral to a robot’s understandingof space. Various tasks such as semantic mapping, pick-andcarrymissions or manipulation involve interaction with objects.Previous work in the field largely builds on the assumption thatthe object in question starts out within the ready sensory reachof the robot. In this work we aim to relax this assumptionby providing the means to perform robust and large-scaleactive visual object search. Presenting spatial relations thatdescribe topological relationships between objects, we thenshow how to use these to create potential search actions. Weintroduce a method for efficiently selecting search strategiesgiven probabilities for those relations. Finally we performexperiments to verify the feasibility of our approach.
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| 9. |
- Aydemir, Alper, et al.
(författare)
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Simultaneous Object Class and Pose Estimation for Mobile Robotic Applications with Minimalistic Recognition
- 2010
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Ingår i: 2010 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA). - 9781424450404 ; , s. 2020-2027
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Konferensbidrag (refereegranskat)abstract
- In this paper we address the problem of simultaneous object class and pose estimation using nothing more than object class label measurements from a generic object classifier. We detail a method for designing a likelihood function over the robot configuration space. This function provides a likelihood measure of an object being of a certain class given that the robot (from some position) sees and recognizes an object as being of some (possibly different) class. Using this likelihood function in a recursive Bayesian framework allows us to achieve a kind of spatial averaging and determine the object pose (up to certain ambiguities to be made precise). We show how inter-class confusion from certain robot viewpoints can actually increase the ability to determine the object pose. Our approach is motivated by the idea of minimalistic sensing since we use only class label measurements albeit we attempt to estimate the object pose in addition to the class.
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| 10. |
- Aydemir, Alper, et al.
(författare)
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What can we learn from 38,000 rooms? : Reasoning about unexplored space in indoor environments
- 2012
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Ingår i: Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on. - : IEEE. - 9781467317375 ; , s. 4675-4682
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Konferensbidrag (refereegranskat)abstract
- Many robotics tasks require the robot to predict what lies in the unexplored part of the environment. Although much work focuses on building autonomous robots that operate indoors, indoor environments are neither well understood nor analyzed enough in the literature. In this paper, we propose and compare two methods for predicting both the topology and the categories of rooms given a partial map. The methods are motivated by the analysis of two large annotated floor plan data sets corresponding to the buildings of the MIT and KTH campuses. In particular, utilizing graph theory, we discover that local complexity remains unchanged for growing global complexity in real-world indoor environments, a property which we exploit. In total, we analyze 197 buildings, 940 floors and over 38,000 real-world rooms. Such a large set of indoor places has not been investigated before in the previous work. We provide extensive experimental results and show the degree of transferability of spatial knowledge between two geographically distinct locations. We also contribute the KTH data set and the software tools to with it.
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