Active Visual Object Search in Unknown Environments Using Uncertain Semantics

Aydemir, Alper; Pronobis, Andrzej; Göbelbecker, Moritz; Jensfelt, Patric

doi:10.1109/tro.2013.2256686

Cited by 103 publications

(80 citation statements)

References 45 publications

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“…This achieved the scaling required, by employing a planner that switches between decision-theoretic and non-decision-theoretic planning on the fly [11], and showed its application to active visual search. Later, Aydemir et al [2] showed the first use of assumptive planning to extend this to object search in an open world. Hanheide et al [13] and Aydemir et al [2] also provided an advance by employing a semantic map that allows belief updating using different knowledge sources.…”

Section: Discussion Of Related Workmentioning

confidence: 99%

“…Later, Aydemir et al [2] showed the first use of assumptive planning to extend this to object search in an open world. Hanheide et al [13] and Aydemir et al [2] also provided an advance by employing a semantic map that allows belief updating using different knowledge sources. There are two advances made here that go beyond that work.…”

Section: Discussion Of Related Workmentioning

confidence: 99%

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Robot task planning and explanation in open and uncertain worlds

Hanheide¹,

Gbelbecker²,

Horn³

et al. 2017

Artificial Intelligence

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129

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A long-standing goal of AI is to enable robots to plan in the face of uncertain and incomplete information, and to handle task failure intelligently. This paper shows how to achieve this. There are two central ideas. The first idea is to organize the robot's knowledge into three layers: instance knowledge at the bottom, commonsense knowledge above that, and diagnostic knowledge on top. Knowledge in a layer above can be used to modify knowledge in the layer(s) below. The second idea is that the robot should represent not just how its actions change the world, but also what it knows or believes. There are two types of knowledge effects the robot's actions can have: epistemic effects (I believe X because I saw it) and assumptions (I'll assume X to be true). By combining the knowledge layers with the models of knowledge effects, we can simultaneously solve several problems in robotics: (i) task planning and execution under uncertainty; (ii) task planning and execution in open worlds; (iii) explaining task failure; (iv) verifying those explanations. The paper describes how the ideas are implemented in a three-layer architecture on a mobile robot platform. The robot implementation was evaluated in five different experiments on object search, mapping, and room categorization.

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Section: Discussion Of Related Workmentioning

confidence: 99%

Section: Discussion Of Related Workmentioning

confidence: 99%

Robot task planning and explanation in open and uncertain worlds

Hanheide¹,

Gbelbecker²,

Horn³

et al. 2017

Artificial Intelligence

Self Cite

129

View full text Add to dashboard Cite

show abstract

“…1 should be shifted to the left). It could be interesting also to extend this work by considering distribution of probability about the location of the victims, starting from results of Aydemir et al (2013). Moreover, a deeper study of the impact of knowledge provided by semantic maps for exploration will be performed.…”

Section: Discussionmentioning

confidence: 99%

A semantically-informed multirobot system for exploration of relevant areas in search and rescue settings

Cipolleschi

Giusto

et al. 2015

Auton Robot

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Coordinated multirobot exploration involves autonomous discovering and mapping of the features of initially unknown environments by using multiple robots. Autonomously exploring mobile robots are usually driven, both in selecting locations to visit and in assigning them to robots, by knowledge of the already explored portions of the environment, often represented in a metric map. In the literature, some works addressed the use of semantic knowledge in exploration, which, embedded in a semantic map, associates spatial concepts (like 'rooms' and 'corridors') with metric entities, showing its effectiveness in improving the total area explored by robots. In this paper, we build on these results and propose a system that exploits semantic information to push robots to explore relevant areas of initially unknown environments, according to a priori information provided by human users. Discovery of relevant areas is significant in some search and rescue settings, in which human rescuers can instruct robots to search for victims in specific areas, for example in cubicles if a disaster happened in an office building during working hours. We propose to speed up the exploration of specific areas by using semantic information both to select locations to visit and to determine the number of robots to allocate to those locations. In this way, for exam-B Alberto Quattrini Li ple, more robots could be assigned to a candidate location in a corridor, so the attached rooms can be explored faster. We tested our semantic-based multirobot exploration system within a reliable robot simulator and we evaluated its performance in realistic search and rescue indoor settings with respect to state-of-the-art approaches.

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“…Wong et al include manipulation actions into the object search, which allows the robot to reason about which objects have to be removed before being able to see the target object [28]. The approach by Aydemir et al [29] is similar to ours in that they also use landmark objects to guide the search for smaller objects inside or on top of the landmarks. While they focus on the probabilistic formulation of the search procedure as a Markov Decision Process, we explore a knowledge-based strategy that exploits formal knowledge about object types, their (likely) spatial relations, and their shape and appearance.…”

Section: Related Workmentioning

confidence: 99%

RoboEarth Semantic Mapping: A Cloud Enabled Knowledge-Based Approach

Riazuelo

Tenorth²,

Marco³

et al. 2015

IEEE Trans. Automat. Sci. Eng.

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Abstract-The vision of the RoboEarth project is to design a knowledge-based system to provide web and cloud services that can transform a simple robot into an intelligent one. In this work we describe the RoboEarth semantic mapping system. The semantic map is composed of (1) an ontology to code the concepts and relations in maps and objects, and (2) a SLAM map providing the scene geometry and the object locations with respect to the robot. We propose to ground the terminological knowledge in the robot perceptions by means of the SLAM map of objects. RoboEarth boosts mapping by providing: (1) a subdatabase of object models relevant for the task at hand, obtained by semantic reasoning, which improves recognition by reducing computation and the false positive rate; (2) the sharing of semantic maps between robots, and (3) software as a service to externalize in the cloud the more intensive mapping computations, while meeting the mandatory hard real time constraints of the robot.To demonstrate the RoboEarth cloud mapping system, we investigate two action recipes that embody semantic map building in a simple mobile robot. The first recipe enables semantic map building for a novel environment while exploiting available prior information about the environment. The second recipe searches for a novel object, with the efficiency boosted thanks to the reasoning on a semantically annotated map. Our experimental results demonstrate that by using RoboEarth cloud services, a simple robot can reliably and efficiently build the semantic maps needed to perform its quotidian tasks. In addition, we show the synergetic relation of the SLAM map of objects that grounds the terminological knowledge coded in the ontology.Note to Practitioners-RoboEarth is a cloud-based knowledge base for robots that transforms a simple robot into an intelligent one thanks to the web services provided. As mapping is a mandatory element on most of the robot systems, we focus on the RoboEarth semantic mapping for robot systems, showing the benefits of the combination of SLAM (Simultaneous Localization And Map building), and knowledge-based reasoning. We show the qualities of our system by means of two experiments: (1) building a map of a novel environment boosted by prior information and (2) efficient searching for a novel object thanks to the knowledgebased reasoning techniques. We can conclude that RoboEarth enables the execution of the proposed methods as web and cloud services that enable advanced perception in a simple robot.

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Active Visual Object Search in Unknown Environments Using Uncertain Semantics

Cited by 103 publications

References 45 publications

Robot task planning and explanation in open and uncertain worlds

Robot task planning and explanation in open and uncertain worlds

A semantically-informed multirobot system for exploration of relevant areas in search and rescue settings

RoboEarth Semantic Mapping: A Cloud Enabled Knowledge-Based Approach

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