Autonomous semantic mapping for robots performing everyday manipulation tasks in kitchen environments

Blodow, Nico; Goron, Lucian Cosmin; Márton, Zoltán-Csaba; Pangercic, Dejan; Rühr, Thomas; Tenorth, Moritz; Beetz, Michael

doi:10.1109/iros.2011.6094665

Cited by 58 publications

(50 citation statements)

References 15 publications

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“…These include 6D localization , autonomous navigation with air vehicles (Heng et al, 2011;Müller et al, 2011), autonomous navigation with humanoid robots (Oßwald et al, 2012;Maier et al, 2012), 3D exploration (Shade andNewman, 2011;Dornhege and Kleiner, 2011), 3D SLAM (Hertzberg et al, 2011), 3D arm navigation (Ciocarlie et al, 2010), semantic mapping (Blodow et al, 2011), and navigation in cluttered environments .…”

Section: Case Studiesmentioning

confidence: 99%

OctoMap: an efficient probabilistic 3D mapping framework based on octrees

et al. 2013

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Three-dimensional models provide a volumetric representation of space which is important for a variety of robotic applications including flying robots and robots that are equipped with manipulators. In this paper, we present an open-source framework to generate volumetric 3D environment models. Our mapping approach is based on octrees and uses probabilistic occupancy estimation. It explicitly represents not only occupied space, but also free and unknown areas. Furthermore, we propose an octree map compression method that keeps the 3D models compact. Our framework is available as an open-source C++ library and has already been successfully applied in several robotics projects. We present a series of experimental results carried out with real robots and on publicly available real-world datasets. The results demonstrate that our approach is able to update the representation efficiently and models the data consistently while keeping the memory requirement at a minimum.

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Section: Case Studiesmentioning

confidence: 99%

OctoMap: an efficient probabilistic 3D mapping framework based on octrees

et al. 2013

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“…Blodow et al extracted semantic information by a laser scanner and a high-resolution 2D camera for a PR2 robot to perform tasks in kitchen environments [99]. Segmentation algorithms were applied to identify and distinguish certain kitchen facilities and their functional components.…”

Section: Semantic Hierarchy Bathroommentioning

confidence: 99%

“…Figure 15. Interaction between the robot and the kitchen facilities [99]. Reproduced with permission from Dr. Pangercic.…”

Section: Semantic Hierarchy Bathroommentioning

confidence: 99%

Extracting Semantic Information from Visual Data: A Survey

Liu

et al. 2016

Robotics

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Abstract:The traditional environment maps built by mobile robots include both metric ones and topological ones. These maps are navigation-oriented and not adequate for service robots to interact with or serve human users who normally rely on the conceptual knowledge or semantic contents of the environment. Therefore, the construction of semantic maps becomes necessary for building an effective human-robot interface for service robots. This paper reviews recent research and development in the field of visual-based semantic mapping. The main focus is placed on how to extract semantic information from visual data in terms of feature extraction, object/place recognition and semantic representation methods.

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“…Whole room data acquisition that requires next best view planning was presented in our earlier work [13] and is based on the information gain approach in which we use costmaps to find those poses that guarantee enough coverage of the unknown space as well as sufficient overlap with the already containing data for successful registration.…”

Section: ) Next Best View Planningmentioning

confidence: 99%

“…This module, initially proposed in [13], uses mesh vertices as seed points around footprint of handles to estimate an initial model of the color distribution of the door. The model consists of the intensity values' medianĩ and median average distance (M AD).…”

Section: ) Generation Of Door and Drawer Hypothesesmentioning

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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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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Autonomous semantic mapping for robots performing everyday manipulation tasks in kitchen environments

Cited by 58 publications

References 15 publications

OctoMap: an efficient probabilistic 3D mapping framework based on octrees

OctoMap: an efficient probabilistic 3D mapping framework based on octrees

Extracting Semantic Information from Visual Data: A Survey

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

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