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Humanoid robotic assistants need capable and comprehensive perception systems that enable them to perform complex manipulation and grasping tasks. This requires the identification and recognition of supporting planes and objects in the world, together with their precise 6D poses. In this paper, we propose a 3D perception system architecture that can robustly fit CAD models in cluttered table setting...
In this paper we present a comprehensive perception system with applications to mobile manipulation and grasping for personal robotics. Our approach makes use of dense 3D point cloud data acquired using stereo vision cameras by projecting textured light onto the scene. To create models suitable for grasping, we extract the supporting planes and model object clusters with different surface geometric...
Humanoid robots performing every day tasks in human environments need a strong perception system in order to operate successfully. As 3D data acquisition devices like laser scanners and time of flight cameras get better and cheaper, we expect three-dimensional perception to become more important. We describe a new method for detecting surfaces of revolution in point clouds within our Sample Consensus...
In this paper we present a new approach for labeling 3D points with different geometric surface primitives using a novel feature descriptor - the Fast Point Feature Histograms, and discriminative graphical models. To build informative and robust 3D feature point representations, our descriptors encode the underlying surface geometry around a point p using multi-value histograms. This highly dimensional...
We report on our experiences regarding the acquisition of hybrid Semantic 3D Object Maps for indoor household environments, in particular kitchens, out of sensed 3D point cloud data. Our proposed approach includes a processing pipeline, including geometric mapping and learning, for processing large input datasets and for extracting relevant objects useful for a personal robotic assistant to perform...
In this paper we present a framework for 3D geometric shape segmentation for close-range scenes used in mobile manipulation and grasping, out of sensed point cloud data. Our proposed approach proposes a robust geometric mapping pipeline for large input datasets that extracts relevant objects useful for a personal robotic assistant to perform manipulation tasks. The objects are segmented out from partial...
In this paper, we investigate the problem of 3D object categorization of objects typically present in kitchen environments, from data acquired using a composite sensor. Our framework combines different sensing modalities and defines descriptive features in various spaces for the purpose of learning good object models. By fusing the 3D information acquired from a composite sensor that includes a color...
This paper presents significant steps towards the online integration of 3D perception and manipulation for personal robotics applications. We propose a modular and distributed architecture, which seamlessly integrates the creation of 3D maps for collision detection and semantic annotations, with a real-time motion replanning framework. To validate our system, we present results obtained during a comprehensive...
In this paper, we present a laser-based approach for door and handle identification. The approach builds on a 3D perception pipeline to annotate doors and their handles solely from sensed laser data, without any a priori model learning. In particular, we segment the parts of interest using robust geometric estimators and statistical methods applied on geometric and intensity distribution variations...
This paper proposes a set of methods for building informative and robust feature point representations, used for accurately labeling points in a 3D point cloud, based on the type of surface the point is lying on. The feature space comprises a multi-value histogram which characterizes the local geometry around a query point, is pose and sampling density invariant, and can cope well with noisy sensor...
In this paper we investigate the usage of persistent point feature histograms for the problem of aligning point cloud data views into a consistent global model. Given a collection of noisy point clouds, our algorithm estimates a set of robust 16D features which describe the geometry of each point locally. By analyzing the persistence of the features at different scales, we extract an optimal set which...
In this paper we investigate the acquisition of 3D functional object maps for indoor household environments, in particular kitchens, out of 3D point cloud data. By modeling the static objects in the world into hierarchical classes in the map, such as cupboards, tables, drawers, and kitchen appliances, we create a library of objects which a household robotic assistant can use while performing its tasks...
This paper presents a novel algorithm for 3D depth estimation using a particle filter (PFDE - particle filter depth estimation) in a monocular vSLAM (visual simultaneous localization and mapping) framework. We present our implementation on an omnidirectional mobile robot equipped with a single monochrome camera and discuss experimental results obtained in our Assistive Kitchen project and its potential...
This paper introduces the assistive kitchen as a comprehensive demonstration and challenge scenario for technical cognitive systems. We describe its hardware and software infrastructure. Within the assistive kitchen application, we select particular domain activities as research subjects and identify the cognitive capabilities needed for perceiving, interpreting, analyzing, and executing these activities...
In this paper we present our work on human action recognition in intelligent environments. We classify actions by looking at a time-sequence of silhouettes extracted from various camera images. By treating time as the third spatial dimension we generate so-called space-time shapes that contain rich information about the actions. We propose a novel approach for recognizing actions, by representing...
This paper describes a mapping system that acquires 3D object models of man-made indoor environments such as kitchens. The system segments and geometrically reconstructs cabinets with doors, tables, drawers, and shelves, objects that are important for robots retrieving and manipulating objects in these environments. The system also acquires models of objects of daily use such glasses, plates, and...
In this paper, we report on a use case of networked sensing technologies in the context of smart homes and specifically the kitchen as scenario for our research. We adapt, use and extend an existing middleware originating from robotics for pervasive computing. We report on initial results towards context recognition in this sensor enriched environment.
We report on ongoing research regarding the development of open mobile robot architectures. Preliminary results of our work show that a great degree of modularity and complexity in mobile robot design can be achieved, by simply using off-the-shelf available hardware components. A hardware robotic platform would not be nearly as efficient, if it wouldn't be sustained by an adequate software system...
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