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Exercising Affordances of Objects: A Part-Based Approach

IEEE roboticS AND AUTOMATION LETTERS 2018年第4期3卷 3465-3472页

作者： Lakani, Safoura Rezapour Rodriguez-Sanchez, Antonio J. Piater, Justus Univ Innsbruck Dept Comp Sci Intelligent & Interact Syst Grp A-6020 Innsbruck Austria

This study shows how learning relations between affordances facilitates performing robotic tasks. Tasks usually involve multiple affordances. For example, for pounding a nail with a hammer, grasp-ability and pound-ability of the hammer are important for performing the pounding task successfully. Furthermore, these affordances are associated with parts of the hammer. In the pounding task, the head of the hammer affords pounding and the handle of the hammer affords grasping. We propose an Red Green Blue-Depth (RGB-D) part-based approach for performing tasks. In this paper, affordances are linked to object parts. We learn affordances associated with manipulation and execution of the tasks, i.e., grasping for manipulation and pounding for execution in the task of pounding a nail. Since affordances are associated with parts, tasks can be executed directly on the objects. Our approach is evaluated in six different robotic tasks on a real robot. We obtained an average of 65% task detection rate superior to the baseline methods and an average of 77% task success rate.

关键词： Red Green Blue-Depth (RGB-D) perception visual learning computer vision for other robotic applications

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Model-Assisted Multiband Fusion for Single Image Enhancement and applications to Robot vision

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IEEE roboticS AND AUTOMATION LETTERS 2018年第4期3卷 2822-2829页

作者： Cho, Younggun Jeong, Jinyong Kim, Ayoung Korea Adv Inst Sci & Technol Dept Civil & Environm Engn Daejeon 34141 South Korea

This paper presents a fast single image enhancement(dagger) that is applicable regardless of channels in various environments. The main idea of the paper is combining model-based and fusion-based dehazing methods, thereby presenting balanced image enhancement while elaborating image details. The proposed method enhances both color and grayscale images without any prior information. Multiband decomposition is utilized to extract the base and detail layers for intensity and Laplacian modules. The proposed ambient map and transmission estimation for the intensity module are effective in restoring the true intensity. Adaptive nonlinear mapping functions adjust details on each residual layer. Through color-corrected reconstruction, our results demonstrate outstanding performance on various types of hazy images. The proposed method is thoroughly validated in terms of conventional image quality comparison. We also provide the evaluation at the application phase from both the semantic (segmentation) and geometric (direct odometry) vision based robotics application.

关键词： computer vision for other robotic applications localization semantic scene understanding

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Unsupervised Deep Homography: A Fast and Robust Homography Estimation Model

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IEEE roboticS AND AUTOMATION LETTERS 2018年第3期3卷 2346-2353页

作者： Ty Nguyen Chen, Steven W. Shivakumar, Shreyas S. Taylor, Camillo Jose Kumar, Vijay Univ Penn Gen Robot Automat Sensing & Percept Lab Philadelphia PA 19104 USA

Homography estimation between multiple aerial images can provide relative pose estimation for collaborative autonomous exploration and monitoring. The usage on a robotic system requires a fast and robust homography estimation algorithm. In this letter, we propose an unsupervised learning algorithm that trains a deep convolutional neural network to estimate planar homographies. We compare the proposed algorithm to traditional-feature-based and direct methods, as well as a corresponding supervised learning algorithm. Our empirical results demonstrate that compared to traditional approaches, the unsupervised algorithm achieves faster inference speed, while maintaining comparable or better accuracy and robustness to illumination variation. In addition, our unsupervised method has superior adaptability and performance compared to the corresponding supervised deep learning method. Our image dataset and a Tensorflow implementation of our work are available at https://***/tynguyen/unsupervisedDeepHomographyRAL2018.

关键词： computer vision for automation deep learning in robotics and automation computer vision for other robotic applications image alignment deep homography

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Asynchronous Corner Detection and Tracking for Event Cameras in Real Time

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IEEE roboticS AND AUTOMATION LETTERS 2018年第4期3卷 3177-3184页

作者： Alzugaray, Ignacio Chli, Margarita Swiss Fed Inst Technol Vis Robot Lab CH-8092 Zurich Switzerland

The recent emergence of bioinspired event cameras has opened up exciting new possibilities in high-frequency tracking, bringing robustness to common problems in traditional vision, such as lighting changes and motion blur. In order to leverage these attractive attributes of the event cameras, research has been focusing on understanding how to process their unusual output: an asynchronous stream of events. With the majority of existing techniques discretizing the event-stream essentially forming frames of events grouped according to their timestamp, we are still to exploit the power of these cameras. In this spirit, this letter proposes a new, purely event-based corner detector, and a novel corner tracker, demonstrating that it is possible to detect corners and track them directly on the event streamin real time. Evaluation on benchmarking datasets reveals a significant boost in the number of detected corners and the repeatability of such detections over the state of the art even in challenging scenarios with the proposed approach while enabling more than a 4x speed-up when compared to the most efficient algorithm in the literature. The proposed pipeline detects and tracks corners at a rate of more than 7.5 million events per second, promising great impact in high-speed applications.

关键词： Visual tracking computer vision for other robotic applications SLAM

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Surfel-Based Next Best View Planning

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IEEE roboticS AND AUTOMATION LETTERS 2018年第4期3卷 3324-3331页

作者： Monica, Riccardo Aleotti, Jacopo Univ Parma Dept Engn & Architecture I-43100 Parma Italy

Next best view (NBV) planning is a central task for automated three-dimensional (3-D) reconstruction in robotics. The most expensive phase of NBV computation is the view simulation step, where the information gain of a large number of candidate sensor poses are estimated. Usually, information gain is related to the visibility of unknown space from the simulated viewpoint. A well-established technique is to adopt a volumetric representation of the environment and to compute the NBV from ray casting by maximizing the number of unknown visible voxels. This letter explores a novel approach for NBV planning based on surfel representation of the environment. Surfels are oriented surface elements, such as circular disks, without explicit connectivity. A new kind of surfel is introduced to represent the frontier between empty and unknown space. Surfels are extracted during 3-D reconstruction, with minimal overhead, from a KinectFusion volumetric representation. Surfel rendering is used to generate images from each simulated sensor pose. Experiments in a real robot setup are reported. The proposed approach achieves better performance than volumetric algorithms based on ray casting implemented on graphics processing unit (GPU), with comparable results in terms of reconstruction quality. Moreover, surfel-based NBV planning can be applied in larger environments as a volumetric representation is limited by GPU memory.

关键词： Autonomous agents range sensing motion and path planning computer vision for other robotic applications

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3DmFV: Three-Dimensional Point Cloud Classification in Real-Time Using Convolutional Neural Networks

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IEEE roboticS AND AUTOMATION LETTERS 2018年第4期3卷 3145-3152页

作者： Ben-Shabat, Yizhak Lindenbaum, Michael Fischer, Anath Techion Israel Inst Technol Dept Mech Engn IL-3200003 Haifa Israel Techion Israel Inst Technol Dept Comp Sci IL-3200003 Haifa Israel

Modern robotic systems are often equipped with a direct three-dimensional (3-D) data acquisition device, e.g., LiDAR, which provides a rich 3-D point cloud representation of the surroundings. This representation is commonly used for obstacle avoidance and mapping. Here, we propose a new approach for using point clouds for another critical robotic capability, semantic understanding of the environment (i.e., object classification). Convolutional neural networks (CNNs), that perform extremely well for object classification in 2-D images, are not easily extendible to 3-D point clouds analysis. It is not straightforward due to point clouds' irregular format and a varying number of points. The common solution of transforming the point cloud data into a 3-D voxel grid needs to address severe accuracy versus memory size tradeoffs. In this letter, we propose a novel, intuitively interpretable, 3-D point cloud representation called 3-D modified Fisher vectors. Our representation is hybrid as it combines a coarse discrete grid structure with continuous generalized Fisher vectors. Using the grid enables us to design a new CNN architecture for real-time point cloud classification. In a series of performance analysis experiments, we demonstrate competitive results or even better than state of the art on challenging benchmark datasets while maintaining robustness to various data corruptions.

关键词： Deep learning in robotics and automation recognition computer vision for transportation computer vision for other robotic applications

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Multimodal Trip Hazard Affordance Detection on Construction Sites

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IEEE roboticS AND AUTOMATION LETTERS 2018年第1期3卷 1-8页

作者： McMahon, Sean Sunderhauf, Niko Upcroft, Ben Milford, Michael Queensland Univ Technol ARC Ctr Excellence Robot Vis Brisbane Qld 4000 Australia

Trip hazards are a significant contributor to accidents on construction and manufacturing sites. Current safety inspections are labor intensive and limited by human fallibility, making automation of trip hazard detection appealing from both a safety and economic perspective. Trip hazards present an interesting challenge to modern learning techniques because they are defined as much by affordance as by object type, for example, wires on a table are not a trip hazard, but can be if lying on the ground. To address these challenges, we conduct a comprehensive investigation into the performance characteristics of 11 different colors and depth fusion approaches, including four fusion and one nonfusion approach, using color and two types of depth images. Trained and tested on more than 600 labeled trip hazards over four floors and 2000 m(2) in an active construction site, this approach was able to differentiate between identical objects in different physical configurations. Outperforming a color-only detector, our multimodal trip detector fuses color and depth information to achieve a 4% absolute improvement in F1-score. These investigative results and the extensive publicly available dataset move us one step closer to assistive or fully automated safety inspection systems on construction sites.

关键词： computer vision for other robotic applications robotics in construction visual learning

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The Improvement of Dropout Strategy Based on Two Evolutionary Algorithms

The Improvement of Dropout Strategy Based on Two Evolutionar...

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IEEE International Conference on robotics and Biomimetics (ROBIO)

作者： Chen, Tianhao Jia, Wenchuan Sun, Yi Shanghai Univ Sch Mechatron Engn & Automat Shanghai Peoples R China Shanghai Key Lab Intelligent Mfg & Robot Shanghai Peoples R China

ISBN: (纸本)9781728103778

Dropout strategy is a simple and common regularization method in the construction of deep network that it can control the status of units in the Dropout layers according to the constant probability values in the training processes to prevent the training from overfitting. However, the probability values of the Dropout strategy are single and decided by users, which means that we need more training iterations to receive better results and avoid less fitting problem. In this paper, two evolutionary algorithms, genetic algorithm and differential evolution algorithm are used to optimize the set probability values of network units to improve dropout strategy and they are proved to be able to increase the accuracy of the original method to about 5%.

关键词： Dropout strategy AI-Based Methods Big Data in robotics and Automation computer vision for other robotic applications

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Accurate Angular Velocity Estimation With an Event Camera

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IEEE roboticS AND AUTOMATION LETTERS 2017年第2期2卷 632-639页

作者： Gallego, Guillermo Scaramuzza, Davide Univ Zurich Robot & Percept Grp CH-8050 Zurich Switzerland

We present an algorithm to estimate the rotational motion of an event camera. In contrast to traditional cameras, which produce images at a fixed rate, event cameras have independent pixels that respond asynchronously to brightness changes, with microsecond resolution. Our method leverages the type of information conveyed by these novel sensors (i.e., edges) to directly estimate the angular velocity of the camera, without requiring optical flow or image intensity estimation. The core of the method is a contrast maximization design. The method performs favorably against ground truth data and gyroscopic measurements from an InertialMeasurement Unit, even in the presence of very high-speed motions (close to 1000 deg/s).

关键词： computer vision for other robotic applications localization

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Spatio-Temporal Video Completion in Spherical Image Sequences

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IEEE roboticS AND AUTOMATION LETTERS 2017年第4期2卷 2032-2039页

作者： Xu, Binbin Pathak, Sarthak Fujii, Hiromitsu Yamashita, Atsushi Asama, Hajime Univ Tokyo Grad Sch Engn Dept Precis Engn Tokyo 1138656 Japan

Spherical cameras are widely used due to their full 360 degrees fields of view. However, a common but severe problem is that anything carrying the camera is always included in the view, occluding visual information. In this letter, we propose a novel method to remove such occlusions in videos taken from a freely moving spherical camera. Our method can recover the occluded background accurately in distorted spherical videos by inpainting the color and motion information of pixels. The missing color and motion information inside the occluded region is iteratively recovered in a coarse-to-fine optimization. Spatial and temporal coherence of color and motion information is enforced, considering spherical image geometry. Initially, feature-point matching is used to remove the effect of camera rotation in order to deal with large pixel displacements. Following this, the iterative optimization process is bootstrapped using a reliable estimate of motion information obtained by interpolating it from surrounding regions. We demonstrate its effectiveness by successfully completing videos and recovering occluded regions recorded in various practical situations and by quantifying it against other state-of-the-art methods.

关键词： computer vision for other robotic applications omnidirectional vision

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