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Smartphone Zombie Detection From LiDAR Point Cloud for Mobile Robot Safety

IEEE ROBOTICS AND AUTOMATION LETTERS 2020年第2期5卷 2256-2263页

作者： Wu, Jiaxu Tamura, Yusuke Wang, Yusheng Woo, Hanwool Moro, Alessandro Yamashita, Atsushi Asama, Hajime Univ Tokyo Grad Sch Engn Dept Precis Engn Tokyo 1138656 Japan Akita Prefectural Univ Dept Intelligent Mechatron Akita 0150055 Japan

Awareness of surrounding and prediction of dangerous situations is essential for autonomous mobile robots, especially during navigation in a human-populated environment. To cope with safety issues, state-of-the-art works have focused on pedestrian detection, tracking, and trajectory prediction. However, only a few studies have been conducted on recognizing some specific types of dangerous behaviors exhibited by pedestrians. Here, we propose a tracking enhanced detection method to recognize people using their smartphones while walking, referred to as smartphone zombie. Features used for pedestrian detection usually involve the rotation variance problem, and in this paper, the drawback is handled by employing motion information from multi-object tracking. The proposed solution has been validated through experiments performed on a newly collected dataset. Results showed that our detector can learn a distinct pattern of the appearance of smartphone zombies. Thus, it can successfully detect them outperforming the existed detection method.

关键词： Robot safety range sensing object detection segmentation and categorization

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Fast Panoptic segmentation Network

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IEEE ROBOTICS AND AUTOMATION LETTERS 2020年第2期5卷 1742-1749页

作者： de Geus, Daan Meletis, Panagiotis Dubbelman, Gijs Eindhoven Univ Technol Dept Elect Engn SPS VCA Grp Mobile Percept Syst Res lab NL-5600 MB Eindhoven Netherlands

In this work, we present an end-to-end network for fast panoptic segmentation. This network, called Fast Panoptic segmentation Network (FPSNet), does not require computationally costly instance mask predictions or rule-based merging operations. This is achieved by casting the panoptic task into a custom dense pixel-wise classification task, which assigns a class label or an instance id to each pixel. We evaluate FPSNet on the Cityscapes and Pascal VOC datasets, and find that FPSNet is faster than existing panoptic segmentation methods, while achieving better or similar panoptic segmentation performance. On the Cityscapes validation set, we achieve a Panoptic Quality score of 55.1%, at prediction times of 114 milliseconds for images with a resolution of 1024 x 2048 pixels. For lower resolutions of the Cityscapes dataset and for the Pascal VOC dataset, FPSNet achieves prediction times as low as 45 and 28 milliseconds, respectively.

关键词： Semantic scene understanding object detection segmentation and categorization deep learning in robotics and automation

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Addressing the Sim2Real Gap in Robotic 3-D Object Classification

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IEEE ROBOTICS AND AUTOMATION LETTERS 2020年第2期5卷 407-413页

作者： Weibel, Jean-Baptiste Patten, Timothy Vincze, Markus TU Wien Automat & Control Inst Vis Robot Lab A-1040 Vienna Austria

Object classification with 3D data is an essential component of any scene understanding method. It has gained significant interest in a variety of communities, most notably in robotics and computer graphics. While the advent of deep learning has progressed the field of 3D object classification, most work using this data type are solely evaluated on CAD model datasets. Consequently, current work does not address the discrepancies existing between real and artificial data. In this work, we examine this gap in an indoor service robotic context by specifically addressing the problem of classification when transferring from artificial CAD models to real reconstructed objects. This is performed by training on ModelNet (CAD models) and evaluating on ScanNet (objects extracted from reconstructed rooms). We show that standard methods do not perform well in this task. We thus introduce a method that carefully samples object parts that are reproducible under various transformations and hence robust. Using graph convolution to classify the composed graph of parts, our method improves upon the baseline. Code is publicly available at https://***/***/cad2real_object_clf.

关键词： Object detection segmentation and categorization deep learning in robotics and automation RGB-D perception

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CNN Based Road User Detection Using the 3D Radar Cube

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IEEE ROBOTICS AND AUTOMATION LETTERS 2020年第2期5卷 1263-1270页

作者： Palffy, Andras Dong, Jiaao Kooij, Julian F. P. Gavrila, Dariu M. Delft Univ Technol Intelligent Vehicles Grp NL-2628 Delft Netherlands

This letter presents a novel radar based, single-frame, multi-class detection method for moving road users (pedestrian, cyclist, car), which utilizes low-level radar cube data. The method provides class information both on the radar target- and object-level. Radar targets are classified individually after extending the target features with a cropped block of the 3D radar cube around their positions, thereby capturing the motion of moving parts in the local velocity distribution. A Convolutional Neural Network (CNN) is proposed for this classification step. Afterwards, object proposals are generated with a clustering step, which not only considers the radar targets;positions and velocities, but their calculated class scores as well. In experiments on a real-life dataset we demonstrate that our method outperforms the state-of-the-art methods both target- and object-wise by reaching an average of 0.70 (baseline: 0.68) target-wise and 0.56 (baseline: 0.48) object-wise F1 score. Furthermore, we examine the importance of the used features in an ablation study.

关键词： Object detection segmentation and categorization sensor fusion deep learning in robotics and automation

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Modeling of Architectural Components for Large-Scale Indoor Spaces From Point Cloud Measurements

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IEEE ROBOTICS AND AUTOMATION LETTERS 2020年第3期5卷 3830-3837页

作者： Lim, Gahyeon Oh, Youjin Kim, Dongwoo Jun, ChangHyun Kang, Jaehyeon Doh, Nakju Korea Univ Sch Elect Engn Seoul 02841 South Korea TeeLabs Seoul 02857 South Korea SK Telecom Seoul 04539 South Korea Korea Inst Ind Technol Robot R&D Grp Ansan 15588 South Korea

In this letter, we propose a method to model architectural components in large-scale indoor spaces from point cloud measurements. The proposed method enables the modeling of curved surfaces, cylindrical pillars, and slanted surfaces, which cannot be modeled using existing approaches. It operates by constructing the architectural points from the raw point cloud after removing non-architectural (objects) points and filling in the holes caused by their exclusion. Then, the architectural points are represented using a set of piece-wise planar segments. Finally, the adjacency graph of the planar segments is constructed to verify the fact that every planar segment is closed. This ensures a watertight mesh model generation. Experimentation using 14 different real-world indoor space datasets and 2 public datasets, comprising spaces of various sizes-from room-scale to large-scale (12,557 m(2)), verify the accuracy of the proposed method in modeling environments with curved surfaces, cylindrical pillars, and slanted surfaces.

关键词： Three-dimensional displays Laser radar Complexity theory Clutter Simultaneous localization and mapping Extraterrestrial measurements Range sensing object detection segmentation and categorization robotics in construction

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RGB-D Recognition and Localization of Cases for Robotic Depalletizing in Supermarkets

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IEEE ROBOTICS AND AUTOMATION LETTERS 2020年第4期5卷 6233-6238页

作者： Arpenti, Pierluigi Caccavale, Riccardo Paduano, Gianmarco Fontanelli, Giuseppe Andrea Lippiello, Vincenzo Villani, Luigi Siciliano, Bruno Univ Napoli Federico II Dept Elect Engn & Informat Technol I-80138 Naples Italy

Integrating a robotic system into the depalletizing process of a supermarket demands a high level of autonomy, based on strong perceptive capabilities. This letter presents a system for detection, recognition, and localization of heterogeneous cases in a depalletizing robotic cell, using a single RGB-D camera. Such a system integrates apriori information on the content of the pallet with data from the RGB-D camera, exploiting a sequence of 2D and 3D model-based computer-vision algorithms. The effectiveness of the proposed methodology is assessed in an experiment where multiple cases and pallet configurations are considered. Finally, a complete depalletizing process is shown.

关键词： Logistics Logistics object detection segmentation and categorization computer vision for automation

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A Unified NMPC Scheme for MAVs Navigation With 3D Collision Avoidance Under Position Uncertainty

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IEEE ROBOTICS AND AUTOMATION LETTERS 2020年第4期5卷 5740-5747页

作者： Mansouri, Sina Sharif Kanellakis, Christoforos Lindqvist, Bjorn Pourkamali-Anaraki, Farhad Agha-mohammadi, Ali-akbar Burdick, Joel Nikolakopoulos, George Lulea Univ Technol Dept Comp Elect & Space Engn Robot Team S-97187 Lulea Sweden Univ Massachusetts Dept Comp Sci Lowell MA 01854 USA CALTECH Jet Prop Lab Pasadena CA 91109 USA CALTECH Div Engn & Appl Sci Pasadena CA 91125 USA

This letter proposes a novel Nonlinear Model Predictive Control (NMPC) framework for Micro Aerial Vehicle (MAV) autonomous navigation in indoor enclosed environments. The introduced framework allows us to consider the nonlinear dynamics of MAVs, nonlinear geometric constraints, while guarantees real-time performance. Our first contribution is to reveal underlying planes within a 3D point cloud, obtained from a 3D lidar scanner, by designing an efficient subspace clustering method. The second contribution is to incorporate the extracted information into the nonlinear constraints of NMPC for avoiding collisions. Our third contribution focuses on making the controller robust by considering the uncertainty of localization in NMPC using Shannon's entropy to define the weights involved in the optimization process. This strategy enables us to track position or velocity references or none in the event of losing track of position or velocity estimations. As a result, the collision avoidance constraints are defined in the local coordinates of the MAV and it remains active and guarantees collision avoidance, despite localization uncertainties, e.g., position estimation drifts. The efficacy of the suggested framework has been evaluated using various simulations in the Gazebo environment.

关键词： Aerial systems: applications collision avoidance autonomous vehicle navigation segmentation and categorization optimization and optimal control

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Unseen Salient Object Discovery for Monocular Robot Vision

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IEEE ROBOTICS AND AUTOMATION LETTERS 2020年第2期5卷 1484-1491页

作者： Chan, Darren M. Riek, Laurel D. Univ Calif San Diego La Jolla CA 92093 USA

A key challenge in robotics is the capability to perceive unseen objects, which can improve a robot's ability to learn from and adapt to its surroundings. One approach is to employ unsupervised, salient object discovery methods, which has shown promise in the computer vision literature. However, most state-of-the-art methods are unsuitable for robotics because they are limited to processing whole video segments before discovering objects, which can constrain real-time perception. To address these gaps, we introduce Unsupervised Foraging of Objects (UFO), a novel, unsupervised, salient object discovery method designed for monocular robot vision. We designed UFO with a parallel discover-prediction paradigm, permitting it to discover arbitrary, salient objects on a frame-by-frame basis, which can help robots to engage in scalable object learning. We compared UFO to the two fastest and most accurate methods for unsupervised salient object discovery (Fast segmentation and Saliency-Aware Geodesic), and show that UFO 6.5 times faster, achieving state-of-the-art precision, recall, and accuracy. Furthermore our evaluation suggests that UFO is robust to real-world perception challenges encountered by robots, including moving cameras and moving objects, motion blur, and occlusion. It is our goal that this work will be used with other robot perception methods, to design robots that can learn novel object concepts, leading to improved autonomy.

关键词： Object detection segmentation and categorization computer vision for other robotic applications

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Learning to Optimally Segment Point Clouds

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IEEE ROBOTICS AND AUTOMATION LETTERS 2020年第2期5卷 875-882页

作者： Hu, Peiyun Held, David Ramanan, Deva Carnegie Mellon Univ Inst Robot Pittsburgh PA 15213 USA Argo AI Pittsburgh PA 15213 USA

We focus on the problem of class-agnostic instance segmentation of LiDAR point clouds. We propose an approach that combines graph-theoretic search with data-driven learning: it searches over a set of candidate segmentations and returns one where individual segments score well according to a data-driven point-based model of "objectness". We prove that if we score a segmentation by the worst objectness among its individual segments, there is an efficient algorithm that finds the optimal worst-case segmentation among an exponentially large number of candidate segmentations. We also present an efficient algorithm for the average-case. For evaluation, we repurpose KITTI 3D detection as a segmentation benchmark and empirically demonstrate that our algorithms significantly outperform past bottom-up segmentation approaches and top-down object-based algorithms on segmenting point clouds.

关键词： Computer vision for automation deep learning in robotics and automation object detection segmentation and categorization semantic scene understanding

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MinneApple: A Benchmark Dataset for Apple Detection and segmentation

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IEEE ROBOTICS AND AUTOMATION LETTERS 2020年第2期5卷 852-858页

作者： Hani, Nicolai Roy, Pravakar Isler, Volkan Univ Minnesota Dept Comp Sci & Engn Minneapolis MN 55455 USA

In this work, we present a new dataset to advance the state-of-the-art in fruit detection, segmentation, and counting in orchard environments. While there has been significant recent interest in solving these problems, the lack of a unified dataset has made it difficult to compare results. We hope to enable direct comparisons by providing a large variety of high-resolution images acquired in orchards, together with human annotations of the fruit on trees. The fruits are labeled using polygonal masks for each object instance to aid in precise object detection, localization, and segmentation. Additionally, we provide data for patch-based counting of clustered fruits. Our dataset contains over 41'0000 annotated object instances in 1000 images. We present a detailed overview of the dataset together with baseline performance analysis for bounding box detection, segmentation, and fruit counting as well as representative results for yield estimation. We make this dataset publicly available and host a CodaLab challenge to encourage a comparison of results on a common dataset. To download the data and learn more about the MinneApple dataset, please see the project website: http://***/***/MinneApple. Up to date information is available online.

关键词： Agricultural automation robotics in agriculture and forestry object detection segmentation and categorization

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