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23rd Conference on Towards Autonomous Robotic Systems (TAROS)

作者： Mantegazza, Dario Redondo, Carlos Espada, Fran Gambardella, Luca M. Giusti, Alessandro Guzzi, Jerome USI SUPSI Dalle Molle Inst Artificial Intelligence IDSIA Lugano Switzerland Hovering Solut Ltd Madrid Spain

ISBN: (纸本)9783031159084;9783031159077

We consider the problem of detecting, in the visual sensing data stream of an autonomous mobile robot, semantic patterns that are unusual (i.e., anomalous) with respect to the robot's previous experience in similar environments. These anomalies might indicate unforeseen hazards and, in scenarios where failure is costly, can be used to trigger an avoidance behavior. We contribute three novel image-based datasets acquired in robot exploration scenarios, comprising a total of more than 200k labeled frames, spanning various types of anomalies. On these datasets, we study the performance of an anomaly detection approach based on autoencoders operating at different scales.

关键词： visual anomaly detection Dataset for robotic vision deep learning for visual perception Robotic perception

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Multi-Scale Feature Aggregation by Cross-Scale Pixel-to-Region Relation Operation for Semantic Segmentation

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IEEE ROBOTICS AND AUTOMATION LETTERS 2021年第3期6卷 5889-5896页

作者： Bai, Yechao Huang, Ziyuan Shen, Lyuyu Guo, Hongliang Ang, Marcelo H., Jr. Rus, Daniela Natl Univ Singapore Dept Mech Engn Singapore 119077 Singapore Singapore MIT Alliance Res & Technol SMART Ctr Future Urban Mobil Singapore 640688 Singapore MIT 77 Massachusetts Ave Cambridge MA 02139 USA

Exploiting multi-scale features has shown great potential in tackling semantic segmentation problems. The aggregation is commonly done with sum or concatenation (concat) followed by convolutional (conv) layers. However, it fully passes down the high-level context to the following hierarchy without considering their interrelation. In this work, we aim to enable the low-level feature to aggregate the complementary context from adjacent high-level feature maps by a cross-scale pixel-to-region relation operation. We leverage cross-scale context propagation to make the long-range dependency capturable even by the high-resolution low-level features. To this end, we employ an efficient feature pyramid network to obtain multi-scale features. We propose a Relational Semantics Extractor (RSE) and Relational Semantics Propagator (RSP) for context extraction and propagation respectively. Then we stack several RSP into an RSP head to achieve the progressive top-down distribution of the context. Experiment results on two challenging datasets Cityscapes and COCO demonstrate that the RSP head performs competitively on both semantic segmentation and panoptic segmentation with high efficiency. It outperforms deeplabV3 [1] by 0.7% with 75% fewer FLOPs (multiply-adds) in the semantic segmentation task.

关键词： Semantic scene understanding deep learning for visual perception automation technologies for smart cities

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Pose Refinement Graph Convolutional Network for Skeleton-Based Action Recognition

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IEEE ROBOTICS AND AUTOMATION LETTERS 2021年第2期6卷 1028-1035页

作者： Li, Shijie Yi, Jinhui Abu Farha, Yazan Gall, Juergen Univ Bonn Comp Vis Grp D-53115 Bonn Germany

With the advances in capturing 2D or 3D skeleton data, skeleton-based action recognition has received an increasing interest over the last years. As skeleton data is commonly represented by graphs, graph convolutional networks have been proposed for this task. While current graph convolutional networks accurately recognize actions, they are too expensive for robotics applications where limited computational resources are available. In this letter, we therefore propose a highly efficient graph convolutional network that addresses the limitations of previous works. This is achieved by a parallel structure that gradually fuses motion and spatial information and by reducing the temporal resolution as early as possible. Furthermore, we explicitly address the issue that human poses can contain errors. To this end, the network first refines the poses before they are further processed to recognize the action. We therefore call the network Pose Refinement Graph Convolutional Network. Compared to other graph convolutional networks, our network requires 86%-93% less parameters and reduces the floating point operations by 89%-96% while achieving a comparable accuracy. It therefore provides a much better trade-off between accuracy, memory footprint and processing time, which makes it suitable for robotics applications.

关键词： deep learning for visual perception recognition

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STA-VPR: Spatio-Temporal Alignment for visual Place Recognition

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IEEE ROBOTICS AND AUTOMATION LETTERS 2021年第3期6卷 4297-4304页

作者： Lu, Feng Chen, Baifan Zhou, Xiang-Dong Song, Dezhen Chinese Acad Sci Chongqing Inst Green & Intelligent Technol Chongqing 400714 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Cent South Univ Sch Automat Changsha 410083 Peoples R China Texas A&M Univ CSE Dept College Stn TX 77843 USA

Recently, the methods based on Convolutional Neural Networks (CNNs) have gained popularity in the field of visual place recognition (VPR). In particular, the features from the middle layers of CNNs are more robust to drastic appearance changes than handcrafted features and high-layer features. Unfortunately, the holistic mid-layer features lack robustness to large viewpoint changes. Here we split the holistic mid-layer features into local features, and propose an adaptive dynamic time warping (DTW) algorithm to align local features from the spatial domain while measuring the distance between two images. This realizes viewpoint-invariant and condition-invariant place recognition. Meanwhile, a local matching DTW (LM-DTW) algorithm is applied to perform image sequence matching based on temporal alignment, which achieves further improvements and ensures linear time complexity. We perform extensive experiments on five representative VPR datasets. The results show that the proposed method significantly improves the CNN-based methods. Moreover, our method outperforms several state-of-the-art methods while maintaining good run-time performance. This work provides a novel way to boost the performance of CNN methods without any re-training for VPR. The code is available at https://***/Lu-Feng/STA-VPR.

关键词： deep learning for visual perception localization vision-based navigation

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Lane Graph Estimation for Scene Understanding in Urban Driving

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IEEE ROBOTICS AND AUTOMATION LETTERS 2021年第4期6卷 8615-8622页

作者： Zurn, Jannik Vertens, Johan Burgard, Wolfram Univ Freiburg D-79085 Freiburg Germany Toyota Res Inst Los Altos CA 94022 USA

Lane-level scene annotations provide invaluable data in autonomous vehicles for trajectory planning in complex environments such as urban areas and cities. However, obtaining such data is time-consuming and expensive since lane annotations have to be annotated manually by humans and are as such hard to scale to large areas. In this work, we propose a novel approach for lane geometry estimation from bird's-eye-view images. We formulate the problem of lane shape and lane connections estimation as a graph estimation problem where lane anchor points are graph nodes and lane segments are graph edges. We train a graph estimation model on multimodal bird's-eye-view data processed from the popular NuScenes dataset and its map expansion pack. We furthermore estimate the direction of the lane connection for each lane segment with a separate model which results in a directed lane graph. We illustrate the performance of our LaneGraphNet model on the challenging NuScenes dataset and provide extensive qualitative and quantitative evaluation. Our model shows promising performance for most evaluated urban scenes and can serve as a step towards automated generation of HD lane annotations for autonomous driving. The dataset will be made available at http://***.

关键词： deep learning for visual perception computer vision for transportation

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L6DNet: Light 6 DoF Network for Robust and Precise Object Pose Estimation With Small Datasets

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IEEE ROBOTICS AND AUTOMATION LETTERS 2021年第2期6卷 2914-2921页

作者： Gonzalez, Mathieu Kacete, Amine Murienne, Albert Marchand, Eric Inst Res & Technol Bcom Artificial Intelligence Lab F-35510 Rennes France Univ Rennes IRISA INRIA Rennes Bretagne Antlantique INRIA IRISACNRS F-35042 Rennes France

Estimating the 3D pose of an object is a challenging task that can be considered within augmented reality or robotic applications. In this letter, we propose a novel approach to perform 6 DoF object pose estimation from a single RGB-D image. We adopt a hybrid pipeline in two stages: data-driven and geometric respectively. The data-driven step consists of a classification CNN to estimate the object 2D location in the image from local patches, followed by a regression CNN trained to predict the 3D location of a set of keypoints in the camera coordinate system. To extract the pose information, the geometric step consists in aligning the 3D points in the camera coordinate system with the corresponding 3D points in world coordinate system by minimizing a registration error, thus computing the pose. Our experiments on the standard dataset LineMod show that our approach is more robust and accurate than state-of-the-art methods. The approach is also validated to achieve a 6 DoF positioning task by visual servoing.

关键词： deep learning for visual perception RGB-D perception visual servoing

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learning Composable Behavior Embeddings for Long-Horizon visual Navigation

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IEEE ROBOTICS AND AUTOMATION LETTERS 2021年第2期6卷 3128-3135页

作者： Meng, Xiangyun Xiang, Yu Fox, Dieter Univ Washington Paul G Allen Sch Comp Sci & Engn Seattle WA 98195 USA NVIDIA NVIDIA Res Seattle WA 98105 USA

learning high-level navigation behaviors has important implications: it enables robots to build compact visual memory for repeating demonstrations and to build sparse topological maps for planning in novel environments. Existing approaches only learn discrete, short-horizon behaviors. These standalone behaviors usually assume a discrete action space with simple robot dynamics, thus they cannot capture the intricacy and complexity of real-world trajectories. To this end, we propose Composable Behavior Embedding (CBE), a continuous behavior representation for long-horizon visual navigation. CBE is learned in an end-to-end fashion;it effectively captures path geometry and is robust to unseen obstacles. We show that CBE can be used to performing memory-efficient path following and topological mapping, saving more than an order of magnitude of memory than behavior-less approaches.

关键词： Navigation visualization Robots Task analysis Trajectory Generators Simultaneous localization and mapping deep learning for visual perception learning from demonstration vision-based navigation

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Attentional Graph Neural Network for Parking-Slot Detection

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IEEE ROBOTICS AND AUTOMATION LETTERS 2021年第2期6卷 3445-3450页

作者： Min, Chen Xu, Jiaolong Xiao, Liang Zhao, Dawei Nie, Yiming Dai, Bin Natl Innovat Inst Def Technol Unmanned Syst Res Ctr Beijing 100871 Peoples R China Peking Univ Sch Elect Engn & Comp Sci Beijing Peoples R China

deep learning has recently demonstrated its promising performance for vision-based parking-slot detection. However, very few existing methods explicitly take into account learning the link information of the marking-points, resulting in complex post-processing and erroneous detection. In this letter, we propose an attentional graph neural network based parking-slot detection method, which refers the marking-points in an around-view image as graph-structured data and utilize graph neural network to aggregate the neighboring information between marking-points. Without any manually designed post-processing, the proposed method is end-to-end trainable. Extensive experiments have been conducted on public benchmark dataset, where the proposed method achieves state-of-the-art accuracy. Code is publicly available at https://***/Jiaolong/gcn-parking-slot.

关键词： deep learning for visual perception graph neural network parking-slot detection recognition

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Ground-Aware Monocular 3D Object Detection for Autonomous Driving

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IEEE ROBOTICS AND AUTOMATION LETTERS 2021年第2期6卷 919-926页

作者： Liu, Yuxuan Yixuan, Yuan Liu, Ming Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Robot & Multipercept Lab Hong Kong Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

Estimating the 3D position and orientation of objects in the environment with a single RGB camera is a critical and challenging task for low-cost urban autonomous driving and mobile robots. Most of the existing algorithms are based on the geometric constraints in 2D-3D correspondence, which stems from generic 6D object pose estimation. We first identify how the ground plane provides additional clues in depth reasoning in 3D detection in driving scenes. Based on this observation, we then improve the processing of 3D anchors and introduce a novel neural network module to fully utilize such application-specific priors in the framework of deep learning. Finally, we introduce an efficient neural network embedded with the proposed module for 3D object detection. We further verify the power of the proposed module with a neural network designed for monocular depth prediction. The two proposed networks achieve state-of-the-art performances on the KITTI 3D object detection and depth prediction benchmarks, respectively.

关键词： Three-dimensional displays Cameras Object detection Two dimensional displays Feature extraction Convolution Neural networks Automation technologies for smart cities deep learning for visual perception object detection segmentation and categorization

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deep Probabilistic Feature-Metric Tracking

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IEEE ROBOTICS AND AUTOMATION LETTERS 2021年第1期6卷 223-230页

作者： Xu, Binbin Davison, Andrew J. Leutenegger, Stefan Imperial Coll London Dept Comp London SW7 2BU England

Dense image alignment from RGB-D images remains a critical issue for real-world applications, especially under challenging lighting conditions and in a wide baseline setting. In this letter, we propose a new framework to learn a pixel-wise deep feature map and a deep feature-metric uncertainty map predicted by a Convolutional Neural Network (CNN), which together formulate a deep probabilistic feature-metric residual of the two-view constraint that can be minimised using Gauss-Newton in a coarse-to-fine optimisation framework. Furthermore, our network predicts a deep initial pose for faster and more reliable convergence. The optimisation steps are differentiable and unrolled to train in an end-to-end fashion. Due to its probabilistic essence, our approach can easily couple with other residuals, where we show a combination with ICP. Experimental results demonstrate state-of-the-art performances on the TUM RGB-D dataset and the 3D rigid object tracking dataset. We further demonstrate our method's robustness and convergence qualitatively.

关键词： deep learning for visual perception SLAM

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