Semantic Modelling of Space

Pronobis, Andrzej; Sjöö, Kristoffer; Zender, Hendrik; Kruijff, Geert-Jan M.; Mozos, Oscar Martinez; Burgard, Wolfram

doi:10.1007/978-3-642-11694-0_5

Cited by 12 publications

(8 citation statements)

References 81 publications

(153 reference statements)

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“…Galindo et al [19] present a multihierarchical approach where they connect the spatial and the conceptual hierarchies via anchoring. Similar to [18] and in contrast to our approach their map does not represent intra-room objects. Nüchter et al [20] propose a semantic map system which up to the classification into floor, ceiling and walls is similar to ours.…”

Section: Related Workmentioning

confidence: 96%

See 1 more Smart Citation

Semantic Object Maps for robotic housework - representation, acquisition and use

Pangercic

Pitzer

Tenorth

et al. 2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-In this article we investigate the representation and acquisition of Semantic Objects Maps (SOMs) that can serve as information resources for autonomous service robots performing everyday manipulation tasks in kitchen environments. These maps provide the robot with information about its operation environment that enable it to perform fetch and place tasks more efficiently and reliably. To this end, the semantic object maps can answer queries such as the following ones: "What do parts of the kitchen look like?", "How can a container be opened and closed?", "Where do objects of daily use belong?", "What is inside of cupboards/drawers?", etc.The semantic object maps presented in this article, which we call SOM + , extend the first generation of SOMs presented by Rusu et al. [1] in that the representation of SOM + is designed more thoroughly and that SOM + also include knowledge about the appearance and articulation of furniture objects. Also, the acquisition methods for SOM + substantially advance those developed in [1] in that SOM + are acquired autonomously and with low-cost (Kinect) instead of very accurate (laser-based) 3D sensors. In addition, perception methods are more general and are demonstrated to work in different kitchen environments. I. INTRODUCTIONRobots that do not know where objects are have to search for them. Robots that do not know how objects look have to guess whether they have fetched the right one. Robots that do not know the articulation models of drawers and cupboards have to open them very carefully in order to not damage them. Thus, robots should store and maintain knowledge about their environment that enables them to perform their tasks more reliably and efficiently. We call the collection of this knowledge the robot's maps and consider maps to be models of the robot's operation environment that serve as information resources for better task performance. Robots build environment maps for many purposes. Most robot maps so far have been proposed for navigation. Robot maps for navigation enable robots to estimate their position in the environment, to check the reachability of the destination and to compute navigation plans. Depending on their purpose maps have to store different kinds of information in different forms. Maps might represent the occupancy of environment of 2D or 3D grid cells, they might contain landmarks or represent the topological structure of the environment. The maps might model objects of daily use, indoor, outdoor, underwater, extraterrestrial surfaces, and aerial environments.

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Section: Related Workmentioning

confidence: 96%

“…Conceptually the closest to our work is the work done in project CoSy [18], where they adopted a multi-layered conceptual spatial model that consists of four maps: metric, navigation, topological and conceptual one. Their system, like ours, uses a SLAM algorithm to generate a metric map.…”

Section: Related Workmentioning

confidence: 99%

Semantic Object Maps for robotic housework - representation, acquisition and use

Pangercic

Pitzer

Tenorth

et al. 2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

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“…Nowadays, given the increasing number of capabilities and applications that are demanded to a mobile robot, e.g. semantic mapping [4], high-level decision making [5], or contextual object recognition [6]- [9], new particularly oriented datasets are required.…”

Section: Introductionmentioning

confidence: 99%

OLT: A Toolkit for Object Labeling applied to robotic RGB-D datasets

Ruiz-Sarmiento

Galindo

Gonzalez-Jimenez

2015

2015 European Conference on Mobile Robots (ECMR)

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Abstract-In this work we present the Object Labeling Toolkit (OLT), a set of software components publicly available for helping in the management and labeling of sequential RGB-D observations collected by a mobile robot. Such a robot can be equipped with an arbitrary number of RGB-D devices, possibly integrating other sensors (e.g. odometry, 2D laser scanners, etc.). OLT first merges the robot observations to generate a 3D reconstruction of the scene from which object segmentation and labeling is conveniently accomplished. The annotated labels are automatically propagated by the toolkit to each RGB-D observation in the collected sequence, providing a dense labeling of both intensity and depth images. The resulting objects' labels can be exploited for many robotic oriented applications, including high-level decision making, semantic mapping, or contextual object recognition. Software components within OLT are highly customizable and expandable, facilitating the integration of already-developed algorithms. To illustrate the toolkit suitability, we describe its application to robotic RGB-D sequences taken in a home environment.

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“…The progenitors of the multi-layered conceptual mapping suggested a hierarchical structure that integrates the metric map, navigation map, topological map and conceptual map; and the map building process is to be regarded as a human-like decomposition and categorization of space [15][16][17]. Similarly, Pronobis and Jensfelt introduced a probabilistic framework combining heterogeneous information, uncertainty and human input for semantic mapping [18].…”

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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Semantic Modelling of Space

Cited by 12 publications

References 81 publications

Semantic Object Maps for robotic housework - representation, acquisition and use

Semantic Object Maps for robotic housework - representation, acquisition and use

OLT: A Toolkit for Object Labeling applied to robotic RGB-D datasets

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

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