Author name cluster

Dejan Pangercic

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

7 papers

1 author row

IROS Conference 2013 Conference Paper

Context aware shared autonomy for robotic manipulation tasks

Thomas Witzig
J. Marius Zöllner
Dejan Pangercic
Sarah Osentoski
Rainer Jäkel
Rüdiger Dillmann

This paper describes a collaborative human-robot system that provides context information to enable more effective robotic manipulation. We take advantage of the semantic knowledge of a human co-worker who provides additional context information and interacts with the robot through a user interface. A Bayesian Network encodes the dependencies between this information provided by the user. The output of this model generates a ranked list of grasp poses best suitable for a given task which is then passed to the motion planner. Our system was implemented in ROS and tested on a PR2 robot. We compared the system to state-of-the-art implementations using quantitative (e. g. success rate, execution times) as well as qualitative (e. g. user convenience, cognitive load) metrics. We conducted a user study in which eight subjects were asked to perform a generic manipulation task, for instance to pour a bottle or move a cereal box, with a set of state-of-the-art shared autonomy interfaces. Our results indicate that an interface which is aware of the context provides benefits not currently provided by other state-of-the-art implementations.

Details

ICRA Conference 2013 Conference Paper

Tracking-based interactive segmentation of textureless objects

Karol Hausman
Ferenc Balint-Benczedi
Dejan Pangercic
Zoltán-Csaba Márton
Ryohei Ueda
Kei Okada
Michael Beetz

This paper describes a textureless object segmentation approach for autonomous service robots acting in human living environments. The proposed system allows a robot to effectively segment textureless objects in cluttered scenes by leveraging its manipulation capabilities. In our pipeline, the cluttered scenes are first statically segmented using state-of-the-art classification algorithm and then the interactive segmentation is deployed in order to resolve this possibly ambiguous static segmentation. In the second step the RGBD (RGB + Depth) sparse features, estimated on the RGBD point cloud from the Kinect sensor, are extracted and tracked while motion is induced into a scene. Using the resulting feature poses, the features are then assigned to their corresponding objects by means of a graph-based clustering algorithm. In the final step, we reconstruct the dense models of the objects from the previously clustered sparse RGBD features. We evaluated the approach on a set of scenes which consist of various textureless flat (e. g. box-like) and round (e. g. cylinder-like) objects and the combinations thereof.

Details

ICRA Conference 2012 Conference Paper

A generalized framework for opening doors and drawers in kitchen environments

Thomas Rühr
Jürgen Sturm
Dejan Pangercic
Michael Beetz
Daniel Cremers

In this paper, we present a generalized framework for robustly operating previously unknown cabinets in kitchen environments. Our framework consists of the following four components: (1) a module for detecting both Lambertian and non-Lambertian (i. e. specular) handles, (2) a module for opening and closing novel cabinets using impedance control and for learning their kinematic models, (3) a module for storing and retrieving information about these objects in the map, and (4) a module for reliably operating cabinets of which the kinematic model is known. The presented work is the result of a collaboration of three PR2 beta sites. We rigorously evaluated our approach on 29 cabinets in five real kitchens located at our institutions. These kitchens contained 13 drawers, 12 doors, 2 refrigerators and 2 dishwashers. We evaluated the overall performance of detecting the handle of a novel cabinet, operating it and storing its model in a semantic map. We found that our approach was successful in 51. 9% of all 104 trials. With this work, we contribute a well-tested building block of open-source software for future robotic service applications.

Details

IROS Conference 2012 Conference Paper

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

Dejan Pangercic
Benjamin Pitzer
Moritz Tenorth
Michael Beetz

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.

Details

IROS Conference 2011 Conference Paper

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

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

In this work we report about our efforts to equip service robots with the capability to acquire 3D semantic maps. The robot autonomously explores indoor environments through the calculation of next best view poses, from which it assembles point clouds containing spatial and registered visual information. We apply various segmentation methods in order to generate initial hypotheses for furniture drawers and doors. The acquisition of the final semantic map makes use of the robot's proprioceptive capabilities and is carried out through the robot's interaction with the environment. We evaluated the proposed integrated approach in the real kitchen in our laboratory by measuring the quality of the generated map in terms of the map's applicability for the task at hand (e. g. resolving counter candidates by our knowledge processing system).

Details

IROS Conference 2010 Conference Paper

Combining perception and knowledge processing for everyday manipulation

Dejan Pangercic
Moritz Tenorth
Dominik Jain
Michael Beetz

This paper describes and discusses the K-COPMAN (Knowledge-enabled Cognitive Perception for Manipulation) system, which enables autonomous robots to generate symbolic representations of perceived objects and scenes and to infer answers to complex queries that require the combination of perception and knowledge processing. Using K-COPMAN, the robot can solve inference tasks such as identifying items that are likely to be missing on a breakfast table. To the programmer K-COPMAN, is presented as a logic programming system that can be queried just like a symbolic knowledge base. Internally, K-COPMAN is realized through a data structure framework together with a library of state-of-the-art perception mechanisms for mobile manipulation in human environments. Key features of K-COPMAN are that it can make a robot environment-aware and that it supports goal-directed as well as passive perceptual processing. K-COPMAN is fully integrated into an autonomous mobile manipulation robot and is realized within the open-source robot library ROS.

Details

IROS Conference 2010 Conference Paper

General 3D modelling of novel objects from a single view

Zoltán-Csaba Márton
Dejan Pangercic
Nico Blodow
Jonathan Kleinehellefort
Michael Beetz

In this paper we present a method for building models for grasping from a single 3D snapshot of a scene composed of objects of daily use in human living environments. We employ fast shape estimation, probabilistic model fitting and verification methods capable of dealing with different kinds of symmetries, and combine these with a triangular mesh of the parts that have no other representation to model previously unseen objects of arbitrary shape. Our approach is enhanced by the information given by the geometric clues about different parts of objects which serve as prior information for the selection of the appropriate reconstruction method. While we designed our system for grasping based on single view 3D data, its generality allows us to also use the combination of multiple views. We present two application scenarios that require complete geometric models: grasp planning and locating objects in camera images.

Details