14 research outputs found

    Cumulative object categorization in clutter

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    In this paper we present an approach based on scene- or part-graphs for geometrically categorizing touching and occluded objects. We use additive RGBD feature descriptors and hashing of graph configuration parameters for describing the spatial arrangement of constituent parts. The presented experiments quantify that this method outperforms our earlier part-voting and sliding window classification. We evaluated our approach on cluttered scenes, and by using a 3D dataset containing over 15000 Kinect scans of over 100 objects which were grouped into general geometric categories. Additionally, color, geometric, and combined features were compared for categorization tasks

    Decomposing CAD models of objects of daily use and reasoning about their functional parts

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    Abstract — Today’s robots are still lacking comprehensive knowledge bases about objects and their properties. Yet, a lot of knowledge is required when performing manipulation tasks to identify abstract concepts like a “handle ” or the “blade of a spatula ” and to ground them into concrete coordinate frames that can be used to parametrize the robot’s actions. In this paper, we present a system that enables robots to use CAD models of objects as a knowledge source and to perform logical inference about object components that have automatically been identified in these models. The system includes several algorithms for mesh segmentation and geometric primitive fitting which are integrated into the robot’s knowledge base as procedural attachments to the semantic representation. Bottom-up segmentation methods are complemented by top-down, knowledge-based analysis of the identified components. The evaluation on a diverse set of object models, downloaded from the Internet, shows that the algorithms are able to reliably detect several kinds of object parts. I

    Introspective Robot Perception using Smoothed Predictions from Bayesian Neural Networks

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    This work focuses on improving uncertainty estimation in the field of object classification from RGB images and demonstrates its benefits in two robotic applications. We employ a Bayesian Neural Network (BNN), and evaluate two practical inference techniques to obtain better uncertainty estimates, namely Concrete Dropout (CDP) and Kronecker-factored Laplace Approximation (LAP). We show a performance increase using more reliable uncertainty estimates as unary potentials within a Conditional Random Field (CRF), which is able to incorporate contextual information as well. Furthermore, the obtained uncertainties are exploited to achieve domain adaptation in a semi-supervised manner, which requires less manual efforts of annotating data. We evaluate our approach on two public benchmark datasets that are relevant for robot perception tasks

    Tracking-based Interactive Segmentation of Textureless Objects

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    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

    RoboSherlock: Unstructured information processing for robot perception

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    We present ROBOSHERLOCK, an open source software framework for implementing perception systems for robots performing human-scale everyday manipulation tasks. In ROBOSHERLOCK, perception and interpretation of realistic scenes is formulated as an unstructured information management (UIM) problem. The application of the UIM principle supports the implementation of perception systems that can answer task-relevant queries about objects in a scene, boost object recognition performance by combining the strengths of multiple perception algorithms, support knowledge-enabled reasoning about objects and enable automatic and knowledge-driven generation of processing pipelines. We demonstrate the potential of the proposed framework by three feasibility studies of systems for real-world scene perception that have been built on top of ROBOSHERLOCK