Search in the real world: Active visual object search based on spatial relations

Aydemir, Alper; Sjöö, Kristoffer; Folkesson, John; Pronobis, Andrzej; Jensfelt, Patric

doi:10.1109/icra.2011.5980495

Cited by 82 publications

(69 citation statements)

References 15 publications

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“…Work done by Aydemir, Sjöö and others in the CogX project ( [7], [13], [8]) introduced the sampling-based approach using object location probability distributions. This approach provides and effective and flexible approach to active visual search which is not restricted by the complexity of optimal object search in the general case [3].…”

Section: Related Workmentioning

confidence: 99%

“…We answer these questions on the basis of QSRs. Starting from the approach of Aydemir et al [7], we assume that the robot has the following information at the beginning of the search: a 2D map, and a 3D map. To this we add the assumption that the robot also knows the poses of a set of known landmark objects, and a set of QSRs between them and other objects.…”

Section: Introductionmentioning

confidence: 99%

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Using Qualitative Spatial Relations for indirect object search

Kunze

Doreswamy

Hawes

2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-Finding objects in human environments requires autonomous mobile robots to reason about potential object locations and to plan to perceive them accordingly. By using information about the 3D structure of the environment, knowledge about landmark objects and their spatial relationship to the sought object, search can be improved by directing the robot towards the most likely object locations.In this paper we have designed, implemented and evaluated an approach for searching for objects on the basis of Qualitative Spatial Relations (QSRs) such as left-of and in-front-of. On the basis of QSRs between landmarks and the sought object we generate metric poses of potential object locations using an extended version of the ternary point calculus and employ this information for view planning. Preliminary results show that search methods based on QSRs are faster and more reliable than methods not considering them.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using Qualitative Spatial Relations for indirect object search

Kunze

Doreswamy

Hawes

2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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“…In this work, more than 50.000 labeled images provided by LabelMe [13] are used to calculate the probabilities in (1). A robot can refine these general relations by updating the probabilities based on observed instances in its own environment and using (1). This way the general set of object-object relations changes according to the specifics of the robot's environment.…”

Section: Learning Object Relationsmentioning

confidence: 99%

“…In [1] the focus is on organizing an efficient search given information about spatial relations between objects in an environment, e.g., 'the book is on the table in room 1'. A decision theoretic strategy selection method is adopted for finding the object using the relations.…”

Section: Introductionmentioning

confidence: 99%

Active Object Search Exploiting Probabilistic Object–Object Relations

Elfring

Jansen

Molengraft

et al. 2014

RoboCup 2013: Robot World Cup XVII

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Abstract. This paper proposes a probabilistic object-object relation based approach for an active object search. An important role of mobile robots will be to perform object-related tasks and active object search strategies deal with the non-trivial task of finding an object in unstructured and dynamically changing environments. This work builds further upon an existing approach exploiting probabilistic object-room relations for selecting the room in which an object is expected to be. Learnt objectobject relations allow to search for objects inside a room via a chain of intermediate objects. Simulations have been performed to investigate the effect of the camera quality on path length and failure rate. Furthermore, a comparison is made with a benchmark algorithm based the same prior knowledge but without using a chain of intermediate objects. An experiment shows the potential of the proposed approach on the AMIGO robot.

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“…It allows communication in a human-friendly way. Further, semantic place information has the potential to facilitate other functions such as mapping [1,2], behavior-based navigation [3], task planning [4] and active object search and rescue [5,6] in an efficient way. Therefore, research on place classification has been an important step in the quest for intelligent human robot interactions.…”

Section: Introductionmentioning

confidence: 99%

Towards generalization of semi-supervised place classification over generalized Voronoi graph

Shi

Kodagoda

2013

Robotics and Autonomous Systems

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With the progress of human-robot interaction (HRI), the ability of a robot to perform highlevel tasks in complex environments is fast becoming an essential requirement. To this end, it is desirable for a robot to understand the environment at both geometric and semantic levels.Therefore in recent years, research towards place classification has been gaining in popularity. After the era of heuristic and rule-based approaches, supervised learning algorithms have been extensively used for this purpose, showing satisfactory performance levels. However, most of those approaches have only been trained and tested in the same environments and thus impede a generalized solution. In this paper, we have proposed a semi-supervised place classification over a generalized voronoi graph (SPCoGVG) which is a semi-supervised learning framework comprised of three techniques: support vector machine (SVM), conditional random field (CRF) and generalized voronoi graph (GVG), in order to improve the generalizability. The inherent problem of training CRF with partially observed data has been solved using a novel parameter estimation algorithm. The effectiveness of the proposed algorithm is validated through extensive analysis of data collected at international university environments.

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Search in the real world: Active visual object search based on spatial relations

Cited by 82 publications

References 15 publications

Using Qualitative Spatial Relations for indirect object search

Using Qualitative Spatial Relations for indirect object search

Active Object Search Exploiting Probabilistic Object–Object Relations

Towards generalization of semi-supervised place classification over generalized Voronoi graph

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