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Efficient Dense Frontier Detection for 2-D Graph SLAM Based on Occupancy Grid Submaps

IEEE roboticS AND AUTOMATION LETTERS 2019年第4期4卷 3569-3576页

作者： Orsulic, Juraj Miklic, Damjan Kovacic, Zdenko Univ Zagreb Fac Elect Engn & Comp Lab Robot & Intelligent Control Syst Zagreb 10000 Croatia RoMb Technol Doo Zagreb 10000 Croatia

In autonomous robot exploration, the frontier is the border in the world map dividing the explored and unexplored space. The frontier plays an important role when deciding where in the environment the robots should go explore next. We consider a modular control system pipeline for autonomous exploration where a 2-D graph simultaneous localization and mapping (SLAM) algorithm based on occupancy grid submaps performs map building and localization, and frontier detection is one of the key system components. We provide an overview of the state of the art in frontier detection and the relevant SLAM concepts and propose a fast specialized frontier detection method which is efficiently constrained to active submaps, yet robust to graph SLAM loop closures.

关键词： Visual-based navigation computer vision for other robotic applications path planning for multiple mobile robots or agents

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Weakly Supervised Fruit Counting for Yield Estimation Using Spatial Consistency

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IEEE roboticS AND AUTOMATION LETTERS 2019年第3期4卷 2348-2355页

作者： Bellocchio, Enrico Ciarfuglia, Thomas A. Costante, Gabriele Valigi, Paolo Univ Perugia Dept Engn I-06125 Perugia Italy

Fruit counting is a fundamental component for yield estimation applications. Most of the existing approaches address this problem by relying on fruit models (i.e., by using object detectors) or by explicitly learning to count. Despite the impressive results achieved by these approaches, all of them need strong supervision information during the training phase. In agricultural applications, manual labeling may require a huge effort or, in some cases, it could be impossible to acquire fine-grained ground truth labels. In this letter, we tackle this problem by proposing a weakly supervised framework that learns to count fruits without the need for task-specific supervision labels. In particular, we devise a novel convolutional neural network architecture that requires only a simple image level binary classifier to detect whether the image contains instances of the fruits or not and combines this information with image spatial consistency constraints. The result is an architecture that learns to count without task-specific labels (e.g., object bounding boxes or the multiplicity of fruit instances in the image). The experiments on three different varieties of fruits (i.e., olives, almonds, and apples) show that our approach reaches performances that are comparable with SotA approaches based on the supervised paradigm.

关键词： Agricultural automation computer vision for other robotic applications deep learning in robotics and automation robotics in agriculture and forestry visual learning

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SilhoNet: An RGB Method for 6D Object Pose Estimation

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IEEE roboticS AND AUTOMATION LETTERS 2019年第4期4卷 3727-3734页

作者： Billings, Gideon Johnson-Roberson, Matthew Univ Michigan Dept Naval Architecture & Marine Engn Ann Arbor MI 48109 USA

Autonomous robot manipulation involves estimating the translation and orientation of the object to be manipulated as a 6-degree-of-freedom (6D) pose. Methods using RGB-D data have shown great success in solving this problem. However, there are situations where cost constraints or the working environment may limit the use of RGB-D sensors. When limited to monocular camera data only, the problem of object pose estimation is very challenging. In this letter, we introduce a novel method called SilhoNet that predicts 6D object pose from monocular images. We use a convolutional neural network pipeline that takes in region of interest proposals to simultaneously predict an intermediate silhouette representation for objects with an associated occlusion mask and a 3D translation vector. The 3D orientation is then regressed fromthe predicted silhouettes. We show that our method achieves better overall performance on theYCB-Video dataset than two networks for 6D pose estimation from monocular image input.

关键词： computer vision for automation computer vision for other robotic applications deep learning in robotics and automation recognition visual learning

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Learning Long-Range Perception Using Self-Supervision From Short-Range Sensors and Odometry

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IEEE roboticS AND AUTOMATION LETTERS 2019年第2期4卷 1279-1286页

作者： Nava, Mirko Guzzi, Jerome Chavez-Garcia, R. Omar Gambardella, Luca M. Giusti, Alessandro USI SUPSI Dalle Molle Inst Artificial Intelligence IDSIA CH-6928 Lugano Switzerland

We introduce a general self-supervised approach to predict the future outputs of a short-range sensor (such as a proximity sensor) given the current outputs of a long-range sensor (such as a camera). We assume that the former is directly related to some piece of information to be perceived (such as the presence of an obstacle in a given position), whereas the latter is information rich but hard to interpret directly. We instantiate and implement the approach on a small mobile robot to detect obstacles at various distances using the video stream of the robot's forward-pointing camera, by training a convolutional neural network on automatically-acquired datasets. We quantitatively evaluate the quality of the predictions on unseen scenarios, qualitatively evaluate robustness to different operating conditions, and demonstrate usage as the sole input of an obstacle-avoidance controller. We additionally instantiate the approach on a different simulated scenario with complementary characteristics, to exemplify the generality of our contribution.

关键词： Range sensing computer vision for other robotic applications deep learning in robotics and automation

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Fusing Body Posture With Facial Expressions for Joint Recognition of Affect in Child-Robot Interaction

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IEEE roboticS AND AUTOMATION LETTERS 2019年第4期4卷 4011-4018页

作者： Filntisis, Panagiotis Paraskevas Efthymiou, Niki Koutras, Petros Potamianos, Gerasimos Maragos, Petros Natl Tech Univ Athens Sch Elect & Comp Engn Zografos 15780 Greece Athena Res & Innovat Ctr Maroussi 15125 Greece Univ Thessaly Dept Elect & Comp Engn Volos 38221 Greece

In this letter, we address the problem of multi-cue affect recognition in challenging scenarios such as child-robot interaction. Toward this goal we propose a method for automatic recognition of affect that leverages body expressions alongside facial ones, as opposed to traditional methods that typically focus only on the latter. Our deep-learning based method uses hierarchical multi-label annotations and multi-stage losses, can be trained both jointly and separately, and offers us computational models for both individual modalities, as well as for the whole body emotion. We evaluate our method on a challenging child-robot interaction database of emotional expressions collected by us, as well as on the GEneva multimodal emotion portrayal public database of acted emotions by adults, and show that the proposed method achieves significantly better results than facial-only expression baselines.

关键词： Gesture posture and facial expressions computer vision for other robotic applications social human-robot interaction deep learning in robotics and automation

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Multi-Task Regression-Based Learning for Autonomous Unmanned Aerial Vehicle Flight Control Within Unstructured Outdoor Environments

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IEEE roboticS AND AUTOMATION LETTERS 2019年第4期4卷 4116-4123页

作者： Maciel-Pearson, Bruna G. Akcay, Samet Atapour-Abarghouei, Amir Holder, Christopher Breckon, Toby P. Univ Durham Dept Comp Sci Durham DH1 3LE England

Increased growth in the global unmanned aerial vehicles (UAV) (drone) industry has expanded possibilities for fully autonomous UAV applications. A particular application which has in part motivated this research is the use of UAV in wide area search and surveillance operations in unstructured outdoor environments. The critical issue with such environments is the lack of structured features that could aid in autonomous flight, such as road lines or paths. In this letter, we propose an end-to-end multi-task regression-based learning approach capable of defining flight commands for navigation and exploration under the forest canopy, regardless of the presence of trails or additional sensors (i.e., GPS). Training and testing are performed using a software in the loop pipeline, which allows for a detailed evaluation against state-of-the-art pose estimation techniques. Our extensive experiments demonstrate that our approach excels in performing dense exploration within the required search perimeter, is capable of covering wider search regions, generalises to previously unseen and unexplored environments and outperforms contemporary state-of-the-art techniques.

关键词： Aerial systems: perception and autonomy autonomous vehicle navigation computer vision for other robotic applications deep learning in robotics and automation

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Camera Pose Estimation Based on PnL With a Known Vertical Direction

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IEEE roboticS AND AUTOMATION LETTERS 2019年第4期4卷 3852-3859页

作者： Lecrosnier, Louis Boutteau, Remi Vasseur, Pascal Savatier, Xavier Fraundorfer, Friedrich Normandie Univ UNTROUEN ESIGELEC IRSEEM F-76000 Rouen France Normandie Univ UNTROUEN UNIHAVRE INSA Rouen LITIS F-76000 Rouen France Graz Univ Technol Inst Comp Graph & Vis A-8010 Graz Austria German Aerosp Ctr DLR Remote Sensing Technol Inst D-82234 Starnberg Germany

In this letter, we address the problem of camera pose estimation using two-dimensional (2D) and 3-D line features, also known as PnL(Perspective-n-Line) with a known vertical direction. The minimal number of line correspondences required to estimate the complete camera pose is 3 (P3L) in the general case, yielding to a minimum of eight possible solutions. Prior knowledge of the vertical direction, such as provided by common sensors (e.g., inertial measurement unit, or IMU), reduces the problem to a 4 DoF problem and outputs a single solution. We benefit this fact to decouple the remaining rotation estimation and the translation estimation and we present a twofold contribution: First, we present a linear formulation of the PnL problem in Plucker lines coordinates with a known vertical direction, including a Gauss-Newton-based orientation and location refinement to compensate IMU sensor noise. Second, we propose a new efficient RANdom SAmple Consensus (RANSAC) scheme for both feature pairing and outliers rejection based solely on rotation estimation from two line pairs. This greatly diminishes the computational cost compared to a RANSAC3 or RANSAC4 scheme. We evaluate our algorithms on synthetic data and on our own real dataset. Experimental results show the state-of-the-art results in term of accuracy and runtime, when facing 2-D noise, 3-D noise, and vertical direction sensor noise.

关键词： computer vision for other robotic applications sensor fusion localization

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GAPLE: Generalizable Approaching Policy LEarning for robotic Object Searching in Indoor Environment

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IEEE roboticS AND AUTOMATION LETTERS 2019年第4期4卷 4003-4010页

作者： Ye, Xin Lin, Zhe Lee, Joon-Young Zhang, Jianming Zheng, Shibin Yang, Yezhou Arizona State Univ Act Percept Grp Sch Comp Informat & Decis Syst Engn Tempe AZ 85281 USA Adobe Inc San Jose CA 94539 USA

We study the problem of learning a generalizable action policy for an intelligent agent to actively approach an object of interest, in an indoor environment, solely from its visual inputs. While scene-driven or recognition-driven visual navigation has been widely studied, prior efforts suffer severely from the limited generalization capability. In this letter, we first argue the object searching task is environment-dependent while the approaching ability is general. To learn a generalizable approaching policy, we present a novel solution dubbed asGeneralizableApproaching Policy LEarning, which adopts two channels of visual features: depth and semantic segmentation, as the inputs to the policy learning module. The empirical studies conducted on the House3D dataset and on a physical platform in a real-world scenario validate our hypothesis, and we further provide in-depth qualitative analysis.

关键词： computer vision for other robotic applications visual-based navigation semantic scene understanding

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PointAtrousNet: Point Atrous Convolution for Point Cloud Analysis

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IEEE roboticS AND AUTOMATION LETTERS 2019年第4期4卷 4035-4041页

作者： Pan, Liang Wang, Pengfei Chew, Chee-Meng Natl Univ Singapore Dept Mech Engn Singapore 119077 Singapore Natl Univ Singapore Temasek Labs Singapore 117411 Singapore

In this letter, we propose a permutation-invariant architecture-PointAtrousNet (PAN), which focuses on exploiting multi-scale local geometric details for point cloud analysis. Inspired by atrous convolution in image domains, we propose the point atrous convolution (PAC) operation. Our PAC can effectively enlarge the receptive field of filters without introducing more parameters or increasing computation amount. In particular, we propose a novel point atrous spatial pyramid pooling module to explicitly exploit neighboring contextual information at multiple scales. Moreover, local geometric details are captured by constructing neighborhood graphs in metric and feature spaces. Experimental results show that our PAN achieves state-of-the-art performance on various point cloud inference applications.

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

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QuEst: A Quaternion-Based Approach for Camera Motion Estimation From Minimal Feature Points

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IEEE roboticS AND AUTOMATION LETTERS 2018年第2期3卷 857-864页

作者： Fathian, Kaveh Pablo Ramirez-Paredes, J. Doucette, Emily A. Curtis, J. Willard Gans, Nicholas R. Univ Texas Dallas Dept Elect Engn Richardson TX 75080 USA Univ Guanajuato Dept Elect Engn Salamanca 36885 Gto Mexico US Air Force Res Lab Munit Directorate Eglin AFB FL 32542 USA

In this letter, we consider the problem of recovering the rotation and translation changes of a moving camera from captured images. This problem is traditionally solved using the epipolar constraint, where the rotation and translation changes are recovered from the essential matrix. We propose a new formulation based on quaternion representation of rotation. Using this formulation, we recover the rotation and translation changes separately without resorting to an essential matrix. We compare the estimation accuracy and execution time of the proposed method with several state-of-the-art algorithms using both synthetic and actual image sequences. On average, our approach shows better accuracy in the presence of noise, but it has a larger execution time. For the benefit of community, we have made the implementation of our algorithm available online and free.

关键词： computer vision for automation computer vision for transportation computer vision for other robotic applications

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