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IEEE International Conference on robotics and Automation (ICRA)

作者： S.A. Stoeter S. Voss N.P. Papanikolopoulos H. Mosemann Artificial Intelligence Robotics and Vision Laboratory Department of Computer Science and Engineering University of Minnesota Minneapolis MN USA Institute for Robotics and Process Control Technical University of Braunschweig Germany

Robotic manipulators in contemporary work-cells are often incapable of solving even simple pick-and-place operations. More specifically, most systems require each object to be supplied in the same and pre-defined way. Introducing regrasping to the production cycle relaxes the constraints imposed on supply mechanisms. Regrasping describes the operation that must be performed whenever an object's pick-up grasp is incompatible with its put-down grasp. The paper presents a novel approach to solve the regrasp problem for robots equipped with parallel-jaw end-effecters. The proposed method first evaluates the object's possible grasps and placements. These are subsequently combined to form grasp-placement-grasp triples. A regrasp sequence leading from the pick-up to the put-down grasp is generated by searching through the resulting space of compatible grasp-placement-grasp triples. The algorithm takes kinematic and geometric constraints of both the manipulator and the objects into account. Computational concerns are addressed by subdividing the calculation into an off-line and a fast online phase.

关键词： Intelligent robots Robot vision systems Belts Path planning Artificial intelligence Laboratories computer science Process control Manipulators Production

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Generating polyhedral convex cones from contact graphs for the identification of assembly process states

Generating polyhedral convex cones from contact graphs for t...

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IEEE International Conference on robotics and Automation (ICRA)

作者： H. Mosemann T. Bierwirth F. Wahl S.A. Stoeter Institute for Robotics and Process Control Technical University of Braunschweig Brunswick Germany Artificial Intelligence Robotics and Vision Laboratory Department of Computer Science and Engineering University of Minnesota Minneapolis USA

A new approach to automatically generate polyhedral convex cones from contact graphs for identification of assembly process states is presented. The state of contact between subassemblies is changing as operation proceeds in manipulative tasks such as part-mating. Measured contact forces and the resulting torques provide information about the contact geometry which is used to guide the assembly operation. In this paper, we present a method to calculate contact graphs for polyhedral subassemblies. The nodes of the contact graph represent contact formations of the subassemblies and the arcs represent neighborhoods of contact formations. This information is used to generate polyhedral convex cones for each contact formation considering friction. These polyhedral convex cones describe static equilibrium for each contact formation and are used to identify the contact state by measuring forces and torques during a part-mating.

关键词： Robotic assembly Force measurement Torque measurement Assembly systems Intelligent robots Friction Robot sensing systems Robot vision systems robotics and automation Uncertainty

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DA-LMR: A Robust Lane Marking Representation for Data Association

DA-LMR: A Robust Lane Marking Representation for Data Associ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Miguel Á ngel Muñ oz-Bañ ó n Jan-Hendrik Pauls Haohao Hu Christoph Stiller Group of Automation Robotics and Computer Vision (AUROVA) University of Alicante Alicante Spain Institute of Measurement and Control Systems Karlsruhe Institute of Technology Karlsruhe Germany

While complete localization approaches are widely studied in the literature, their data association and data representation subprocesses usually go unnoticed. However, both are a key part of the final pose estimation. In this work, we present DA-LMR (Delta-Angle Lane Marking Representation), a robust data representation in the context of localization approaches. We propose a representation of lane markings that encodes how a curve changes in each point and includes this information in an additional dimension, thus providing a more detailed geometric structure description of the data. We also propose DC-SAC (Distance-Compatible Sample Consensus), a data association method. This is a heuristic version of RANSAC that dramatically reduces the hypothesis space by distance compatibility restrictions. We compare the presented methods with some state-of-the-art data representation and data association approaches in different noisy scenarios. The DA-LMR and DC-SAC produce the most promising combination among those compared, reaching 98.1 % in precision and 99.7% in recall for noisy data with 0.5 m of standard deviation.

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Testing SPARUS II AUV, an open platform for industrial, scientific and academic applications

arXiv

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arXiv 2018年

作者： Carreras, Marc Candela, Carles Ribas, David Palomeras, Narcís Magí, Lluís Mallios, Angelos Vidal, Eduard Vidal, Èric Ridao, Pere Computer Vision and Robotics Institute Universitat de Girona Parc Científic i Tecnològic UdG Girona17003 Spain

This paper describes the experience of preparing and testing the SPARUS II AUV in different applications. The AUV was designed as a lightweight vehicle combining the classical torpedo-shape features with the hovering capability. The robot has a payload area to allow the integration of different equipment depending on the application. The software architecture is based on ROS, an open framework that allows an easy integration of many devices and systems. Its flexibility, easy operation and openness makes the SPARUS II AUV a multipurpose platform that can adapt to industrial, scientific and academic applications. Five units were developed in 2014, and different teams used and adapted the platform for different applications. The paper describes some of the experiences in preparing and testing this open platform to different applications. Copyright © 2018, The Authors. All rights reserved.

关键词： Autonomous underwater vehicles

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Improving the efficiency and effectiveness of railcar safety appliance inspection using machine vision technology

Improving the efficiency and effectiveness of railcar safety...

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ASME/IEEE Joint Conference on Rail

作者： R. Edwards C.P.L. Barkan J.M. Hart S. Todorovic N. Ahuja Department of Civil and Environmental Engineering Newmark Civil Engineering Laboratory University of Illinois Urbana-Champaign Urbana IL USA Computer Vision and Robotics Laboratory Beckman Institute for Advanced Science and Technology Urbana IL USA Department of Electrical and Computer Engineering Computer Vision and Robotics Laboratory Beckman Institute for Advanced Science and Technology Urbana IL USA

Before a train departs a yard, many aspects of the freight cars and locomotives undergo inspection, including their safety appliances. Safety appliances are handholds, ladders and other objects that serve as the interface between humans and railcars during transportation. Federal safety rules govern the design and condition of safety appliances. The current car inspection process is primarily visual making it laborious, redundant, and generally lacking of memory. There exists a potential to increase both the effectiveness and efficiency of safety appliance inspections by utilizing machine vision technology to enhance the railcar inspection process. Machine vision consists of capturing digital video and using algorithms capable of detecting and analyzing the particular objects or patterns of interest. computer algorithms can objectively inspect railcars without tiring or becoming distracted and can also focus on certain parts of the railcar not easily seen by an inspector on the ground. Thus far, algorithms have been developed that can detect deformed ladders, handholds, and brake wheels on open-top gondolas and hoppers. Next, visual learning will be employed to teach the algorithm the differences between Federal Railroad Administration (FRA) safety appliance defects and other types of deformation not requiring a car to be bad ordered. The final product will be a wayside inspection system capable of detecting safety appliance defects on passing railcars.

关键词： Railway safety Home appliances Inspection Machine vision Vehicle safety US Department of Transportation Humans Rail transportation Object detection Pattern analysis

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FRAGG-Map: Frustum Accelerated GPU-Based Grid Map

FRAGG-Map: Frustum Accelerated GPU-Based Grid Map

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Michele Grimaldi Narcis Palomeras Ignacio Carlucho Yvan R. Petillot Pere Ridao Rodriguez Computer Vision and Robotics Institute University of Girona Girona Spain School of Engineering & Physical Sciences Heriot-Watt University Edinburgh UK

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In robotics, occupancy grids serve as required repositories of information about the environment in numerous applications. One such critical application is Simultaneous Localization and Mapping (SLAM), where robots dynamically scan and explore their surroundings while in motion. In the context of extended-duration missions, it becomes imperative to confront the complexities linked to the expansion of occupancy grids as well as handling loop closure detection. These challenges primarily revolve around two key aspects: enabling the seamless expansion of the map on multiple occasions, thus avoiding the need to map smaller regions in numerous separate missions, and ensuring real-time updates to the map to sustain the robot’s knowledge base and enhance its responsiveness. To address these challenges, we introduce an innovative map called Frustum Accelerated GPU-Based Grid Map (FRAGG-Map). This map adopts a highly parallelizable 3D grid structure and leverages the power of CUDA kernels to facilitate efficient insertion of point-clouds and enables real-time updates of the map. FRAGG-Map identifies the portions of the map that require updates and utilises the GPU to update them, significantly enhancing computational performance. Our results show that FRAGG-Map can run 31 times faster than OctoMap, significantly outperforming state-of-the-art methods.

关键词： Tracking loops Simultaneous localization and mapping Three-dimensional displays Semantics Knowledge based systems Graphics processing units Real-time systems Complexity theory Kernel Intelligent robots

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Attenuating stereo pixel-locking via affine window adaptation

Attenuating stereo pixel-locking via affine window adaptatio...

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IEEE International Conference on robotics and Automation (ICRA)

作者： A.N. Stein A. Huertas L. Matthies Robotics Institute Carnegie Mellon University Pittsburgh PA USA Computer Vision Group Jet Propulsion Laboratory Pasadena CA USA

For real-time stereo vision systems, the standard method for estimating sub-pixel stereo disparity given an initial integer disparity map involves fitting parabolas to a matching cost function aggregated over rectangular windows. This results in a phenomenon known as pixel-locking, which produces artificially-peaked histograms of sub-pixel disparity. These peaks correspond to the introduction of erroneous ripples or waves in the 3D reconstruction of truly flat surfaces. Since stereo vision is a common input modality for autonomous vehicles, these inaccuracies can pose a problem for safe, reliable navigation. This paper proposes a new method for sub-pixel stereo disparity estimation, based on ideas from Lucas-Kanade tracking and optical flow, which substantially reduces the pixel-locking effect. In addition, it has the ability to correct much larger initial disparity errors than previous approaches and is more general as it applies not only to the ground plane. We demonstrate the method on synthetic imagery as well as real stereo data from an autonomous outdoor vehicle

关键词： Stereo vision Remotely operated vehicles Mobile robots Real time systems Cost function Histograms Image reconstruction Surface waves Optical surface waves Surface fitting

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S+T: An algorithm for distributed multirobot task allocation based on services for improving robot cooperation

S+T: An algorithm for distributed multirobot task allocation...

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2008 IEEE International Conference on robotics and Automation (ICRA 2008), vol.7

作者： Antidio Viguria Ivan Maza Anibal Ollero School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta USA Robotics Vision and Control Group University of Seville Seville Spain

In this paper, we present a distributed market-based algorithm called S+T, which solves the multi-robot task allocation (MRTA) problem in applications that require the cooperation among the robots to accomplish all the tasks. If a robot cannot execute a task by itself, it asks for help and, if possible, another robot will provide the required service. In the paper, tasks consisting in transmitting data in real-time that could require communication relay services are considered. On the other hand, the parameters of the algorithm can be adapted to give priority to either the execution time or the energy consumption in the mission. The potential generation of deadlocks associated to the relation between tasks and services is studied, and as an original result, a distributed algorithm that prevent them is proposed. The algorithm has been tested in simulations that illustrate the main features of the S+T algorithm.

关键词： Robot kinematics robotics and automation Robot vision systems System recovery Protocols Costs USA Councils Relays Energy consumption Distributed algorithms

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Automated sub-cortical brain structure segmentation combining spatial and deep convolutional features

arXiv

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arXiv 2017年

作者： Kushibar, Kaisar Valverde, Sergi González-Villà, Sandra Bernal, Jose Cabezas, Mariano Oliver, Arnau Lladó, Xavier Institute of Computer Vision and Robotics University of Girona Ed. P-IV Campus Montilivi University of Girona Girona17003 Spain

Sub-cortical brain structure segmentation in Magnetic Resonance Images (MRI) has attracted the interest of the research community for a long time because morphological changes in these structures are related to different neurodegenerative disorders. However, manual segmentation of these structures can be tedious and prone to variability, highlighting the need for robust automated segmentation methods. In this paper, we present a novel convolutional neural network based approach for accurate segmentation of the sub-cortical brain structures that combines both convolutional and prior spatial features for improving the segmentation accuracy. In order to increase the accuracy of the automated segmentation, we propose to train the network using a restricted sample selection to force the network to learn the most difficult parts of the structures. We evaluate the accuracy of the proposed method on the public MICCAI 2012 challenge and IBSR 18 datasets, comparing it with different available state-of-the-art methods and other recently proposed deep learning approaches. On the MICCAI 2012 dataset, our method shows an excellent performance comparable to the best challenge participant strategy, while performing significantly better than state-of-the-art techniques such as FreeSurfer and FIRST. On the IBSR 18 dataset, our method also exhibits a significant increase in the performance with respect to not only FreeSurfer and FIRST, but also comparable or better results than other recent deep learning approaches. Moreover, our experiments show that both the addition of the spatial priors and the restricted sampling strategy have a significant effect on the accuracy of the proposed method. In order to encourage the reproducibility and the use of the proposed method, a public version of our approach is available to download for the neuroimaging community. Copyright © 2017, The Authors. All rights reserved.

关键词： Convolution

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Conditional Sequential Modulation for Efficient Global Image Retouching 16th

Conditional Sequential Modulation for Efficient Global Image...

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16th European Conference on computer vision, ECCV 2020

作者： He, Jingwen Liu, Yihao Qiao, Yu Dong, Chao ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT - SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Beijing China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030586003

Photo retouching aims at enhancing the aesthetic visual quality of images that suffer from photographic defects such as over/under exposure, poor contrast, inharmonious saturation. Practically, photo retouching can be accomplished by a series of image processing operations. In this paper, we investigate some commonly-used retouching operations and mathematically find that these pixel-independent operations can be approximated or formulated by multi-layer perceptrons (MLPs). Based on this analysis, we propose an extremely light-weight framework - Conditional Sequential Retouching Network (CSRNet) - for efficient global image retouching. CSRNet consists of a base network and a condition network. The base network acts like an MLP that processes each pixel independently and the condition network extracts the global features of the input image to generate a condition vector. To realize retouching operations, we modulate the intermediate features using Global Feature Modulation (GFM), of which the parameters are transformed by condition vector. Benefiting from the utilization of 1 × 1 convolution, CSRNet only contains less than 37 k trainable parameters, which is orders of magnitude smaller than existing learning-based methods. Extensive experiments show that our method achieves state-of-the-art performance on the benchmark MIT-Adobe FiveK dataset quantitively and qualitatively. Code is available at https://***/hejingwenhejingwen/CSRNet. © 2020, Springer Nature Switzerland AG.

关键词： Pixels

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