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We present an approach to learning features that represent the local geometry around a point in an unstructured point cloud. Such features play a central role in geometric registration, which supports diverse applications in robotics and 3D vision. Current state-of-the-art local features for unstructured point clouds have been manually crafted and none combines the desirable properties of precision,...
Object representation is one of the most challenging tasks in robotics because it must provide reliable information in real-time to enable the robot to physically interact with the objects in its environment. To ensure reliability, a global object descriptor must be computed based on a unique and repeatable object reference frame. Moreover, the descriptor should contain enough information enabling...
This paper presents a method to analyse 3D planar surfaces and to measure variations on it. The method is oriented to the detection of deformations on the elastic object surfaces formed by flat faces. These deformations are usually caused when two bodies, a solid and another elastic object, come in contact and there are contact pressures among their faces. Our method describes a strategy to model...
We present a descriptor estimator for surface-based 3D input data for coarse localization of mobile robots. From the input pointclouds surfaces are reconstructed and simplified to detect stable keypoints which are used to evaluate rotation and translation invariant features. The invariance is achieved by transforming the triangulated input data into the frequency domain by Fourier transformation and...
Recognizing and grasping object of robots are two crucial issues for robots accomplishing complicated tasks in unstructured environments, where object representation is a key premise problem. In this paper, we study the representation methods for (1) object recognition, SHOT descriptor is further described by using Tsallis entropy, which greatly reduces the storage and the computation time; (2) grasp...
In order for robots to function in unstructured environments in interaction with humans, they must be able to reason about the world in a semantic meaningful way. An essential capability is to segment the world into semantic plausible object hypotheses. In this paper we propose a general framework which can be used for reasoning about objects and their functionality in manipulation activities. Our...
Visual recognition in multi-robot systems has a peculiar problem in that an observation from a different viewpoint gives a different perspective. Also, a representation of the same target object is highly affected by such a viewpoint or environmental condition. Hence it has become a challenge to realize cognitive sharing of the object or ROI between the robots without a help of artificial markers...
We present a method by which a robot learns to predict effective contact locations for pushing as a function of object shape. The robot performs push experiments at many contact locations on multiple objects and records local and global shape features at each point of contact. Each trial attempts to either push the object in a straight line or to rotate the object to a new orientation. The robot observes...
Object recognition and especially object class recognition is and will be a key capability in home robotics when robots have to tackle manipulation tasks and grasp new objects or just have to search for objects. The goal is to have a robot classify 'never before seen objects' at first occurrence in a single view in a fast and robust manner. The classification task can be seen as a matching problem,...
Image-based environment representations capture the appearance of the surroundings of a mobile robot and are useful for the detection of novelty. However, image-based novelty detection can be impaired by illumination effects. In this paper we present an approach for the image-based detection of novel objects in a scene under varying lighting conditions and in the presence of objects with specular...
Retrieving a large collection of environment maps built by mapper robots is a key problem for mobile robot self-localization. The current paper studies this map retrieval problem from a novel perspective of a multi-scale Bag-Of-Features (BOF) approach. In general, multi-scale approach is advantageous in capturing both the global structure and the local details of a given map. On the other hand, BOF...
We present the Viewpoint Feature Histogram (VFH), a descriptor for 3D point cloud data that encodes geometry and viewpoint. We demonstrate experimentally on a set of 60 objects captured with stereo cameras that VFH can be used as a distinctive signature, allowing simultaneous recognition of the object and its pose. The pose is accurate enough for robot manipulation, and the computational cost is low...
Learning to recognize objects from a small number of example views is a difficult problem of robot vision, of particular importance to assistance robots who are taught by human users. Here we present an approach that combines bottom-up recognition of matching patterns and top-down estimation of pose parameters in a recurrent loop that improves on previous efforts to reconcile invariance of recognition...
For efficient viewing of the comics, it is desirable for the user to non-linearly control the viewing order of panels in comics and manipulate size and colors of objects in the specified panel. In this paper, we present human robot interaction techniques using face pose, facial expression and hand gesture for viewing comics on the robot. The user's face pose is used to control viewing order of panels...
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