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arXiv 2018年

作者： Khaliq, Ahmad Ehsan, Shoaib Chen, Zetao Milford, Michael McDonald-Maier, Klaus Embedded and Intelligent System Lab School of Computer Science and Electronic Engineering University of Essex Colchester United Kingdom Vision for Robotics Lab Eth Zurich Zurich8092 Switzerland Australian Centre for Robotic Vision School of Electrical Engineering and Computer Science Queensland University of Technology Brisbane Australia

This paper presents a lightweight visual place recognition approach, capable of achieving high performance with low computational cost, and feasible for mobile robotics under significant viewpoint and appearance changes. Results on several benchmark datasets confirm an average boost of 13% in accuracy, and 12x average speedup relative to state-of-the-art methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Robot applications

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2D-3D camera fusion for visual odometry in outdoor environments

2D-3D camera fusion for visual odometry in outdoor environme...

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2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Danda Pani Paudel Cédric Demonceaux Adlane Habed Pascal Vasseur In So Kweon Le2i University of Burgundy France ICube University of Strasbourg France LITIS University of Rouen France Robotics and Computer Vision Lab KAIST Korea

Accurate estimation of camera motion is very important for many robotics applications involving SfM and visual SLAM. Such accuracy is attempted by refining the estimated motion through nonlinear optimization. As many modern robots are equipped with both 2D and 3D cameras, it is both highly desirable and challenging to exploit data acquired from both modalities to achieve a better localization. Existing refinement methods, such as Bundle adjustment and loop closing, may be employed only when precise 2D-to-3D correspondences across frames are available. In this paper, we propose a framework for robot localization that benefits from both 2D and 3D information without requiring such accurate correspondences to be established. This is carried out through a 2D-3D based initial motion estimation followed by a constrained nonlinear optimization for motion refinement. The initial motion estimation finds the best possible 2D-to-3D correspondences and localizes the cameras with respect the 3D scene. The refinement step minimizes the projection errors of 3D points while preserving the existing relationships between images. The problems of occlusion and that of missing scene parts are handled by comparing the image-based reconstruction and 3D sensor measurements. The effect of data inaccuracies is minimized using an M-estimator based technique. Our experiments have demonstrated that the proposed framework allows to obtain a good initial motion estimate and a significant improvement through refinement.

关键词： Cameras Three-dimensional displays Image reconstruction Optimization Lasers Robot sensing systems

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PointAtrousGraph: Deep Hierarchical Encoder-Decoder with Point Atrous Convolution for Unorganized 3D Points

PointAtrousGraph: Deep Hierarchical Encoder-Decoder with Poi...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Liang Pan Chee-Meng Chew Gim Hee Lee Advanced Robotics Centre National University of Singapore Department of Mechanical Engineering National University of Singapore Computer Vision and Robotic Perception (CVRP) Lab National University of Singapore

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

Motivated by the success of encoding multi-scale contextual information for image analysis, we propose our PointAtrousGraph (PAG) - a deep permutation-invariant hierarchical encoder-decoder for efficiently exploiting multi-scale edge features in point clouds. Our PAG is constructed by several novel modules, such as Point Atrous Convolution (PAC), Edgepreserved Pooling (EP) and Edge-preserved Unpooling (EU). Similar with atrous convolution, our PAC can effectively enlarge receptive fields of filters and thus densely learn multi-scale point features. Following the idea of non-overlapping maxpooling operations, we propose our EP to preserve critical edge features during subsampling. Correspondingly, our EU modules gradually recover spatial information for edge features. In addition, we introduce chained skip subsampling/upsampling modules that directly propagate edge features to the final stage. Particularly, our proposed auxiliary loss functions can further improve our performance. Experimental results show that our PAG outperform previous state-of-the-art methods on various 3D semantic perception applications.

关键词： Three-dimensional displays Picture archiving and communication systems Convolution Semantics Image edge detection Task analysis Decoding

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Fast Fog Detection for Camera Based Advanced Driver Assistance Systems

Fast Fog Detection for Camera Based Advanced Driver Assistan...

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IEEE International Conference on Intelligent Transportation Systems

作者： Rita Spinneker Carsten Koch Su-Birm Park Jason Jeongsuk Yoon Hochschule Emden/Leer Automation and Robotics (I2AR) Emden Germany LG Electronics Future IT Lab Computer Vision Team Seoul Republic of Korea

In an increasing number of cars, the driver is supported by Advanced Driver Assistance Systems (ADAS). In particular camera based ADAS are a key component for further improvements in safety and driving comfort. While imaging sensors are performing well under good weather conditions, their efficiency suffers under adverse environmental influences such as heavy rain, fog or snow. To handle such optical threats and to estimate information quality of cameras in order to warn the assistance system of possible critical working conditions, a self-diagnosis mechanism is of great importance for reliable operation of an optical ADAS. In this paper an approach of camera based fog detection as part of a self-diagnosis mechanism for ADAS based on the blurring effect of fog is presented. The encouraging results of our experiments have shown that the presented approach of analysing the power spectrum slope (PSS) of a small image block in close proximity to the vanishing point enables a fast discrimination of street scenes with and without fog.

关键词： Cameras Vehicles Roads Integrated optics Optical imaging Optical sensors Automotive components

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Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation

arXiv

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arXiv 2024年

作者： Meraz, Md. Ansari, Md Afzal Javed, Mohammed Chakraborty, Pavan Center of Intelligent Robotics Lab Department of Information Technology Computer Vision & Biometrics Lab Department of Information Technology Indian Institute of Information Technology Allahabad U.P Prayagraj India

In recent years, the challenge of 3D shape analysis within point cloud data has gathered significant attention in computer vision. Addressing the complexities of effective 3D information representation and meaningful feature extraction for classification tasks remains crucial. This paper presents Point-GR, a novel deep learning architecture designed explicitly to transform unordered raw point clouds into higher dimensions while preserving local geometric features. It introduces residual-based learning within the network to mitigate the point permutation issues in point cloud data. The proposed Point-GR network significantly reduced the number of network parameters in Classification and PartSegmentation compared to baseline graph-based networks. Notably, the Point-GR model achieves a state-of-the-art scene segmentation mean IoU of 73.47% on the S3DIS benchmark dataset, showcasing its effectiveness. Furthermore, the model shows competitive results in Classification and Part-Segmentation tasks. © 2024, CC BY.

关键词： Graph neural networks

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Image Restoration for Terahertz Image Based on Complex-Valued Deconvolution 8

Image Restoration for Terahertz Image Based on Complex-Value...

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8th Asia-Pacific Conference on Antennas and Propagation, APCAP 2019

作者： Ning, Wei Qi, Feng Wang, Jinkuan Northeastern University School of Computer Science and Engineering Shenyang110169 China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China Key Lab of Image Understanding and Computer Vision Liaoning Province Shenyang110016 China

ISBN: (纸本)9781665400541

According to the unique characteristics of terahertz (THz) waves, THz imaging has become a hot topic in widely application areas. However, the imaging resolution is constrained by its long wavelength. Generally, the deconvolution is always used to reconstruct the object function. In this paper, a method that combining the classic deconvolution algorithm with the complex-value data processing is investigated. It is posed that the proposed complex-number deconvolution achieves better performance compared to the real-valued deconvolution. © 2019 IEEE.

关键词： Deconvolution

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Scene Representations for Robotic Spatial Perception

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Control, robotics, and Autonomous Systems 1000年第1期8卷 351-377页

作者： Ruben Mascaro Margarita Chli 1Vision for Robotics Lab ETH Zurich Zurich Switzerland email: rmascaro@ethz.ch chlim@ethz.ch 2Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

The ability of a robot to build a persistent, accurate, and actionable model of its surroundings through sensor data in a timely manner is crucial for autonomous operation. While representing the world as a point cloud might be sufficient for localization, denser scene representations are required for obstacle avoidance. On the other hand, higher-level semantic information is often crucial for breaking down the necessary steps to autonomously complete a complex task, such as cooking. So the looming question is, What is a suitable scene representation for the robotic task at hand? This survey provides a comprehensive review of key approaches and frameworks driving progress in the field of robotic spatial perception, with a particular focus on the historical evolution and current trends in representation. By categorizing scene modeling techniques into three main types—metric, metric–semantic, and metric–semantic–topological—we discuss how spatial perception frameworks are transitioning from building purely geometric models of the world to more advanced data structures incorporating higher-level concepts, such as the notion of object instances and places. Special emphasis is placed on approaches for real-time simultaneous localization and mapping, their integration with deep learning for enhanced robustness and scene understanding, and their ability to handle scene dynamicity as some of the hottest topics of interest driving robotics research today. We conclude with a discussion of ongoing challenges and future research directions in the quest to develop robust and scalable spatial perception systems suitable for long-term autonomy.

关键词： spatial perception simultaneous localization and mapping SLAM semantic SLAM 3D scene graphs scene representations robotics

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RTM3D: Real-Time Monocular 3D Detection from Object Keypoints for Autonomous Driving 16th

RTM3D: Real-Time Monocular 3D Detection from Object Keypoin...

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16th European Conference on computer vision, ECCV 2020

作者： Li, Peixuan Zhao, Huaici Liu, Pengfei Cao, Feidao Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang China Key Lab of Image Understanding and Computer Vision ShenyangLiaoning China

ISBN: (纸本)9783030585792

In this work, we propose an efficient and accurate monocular 3D detection framework in single shot. Most successful 3D detectors take the projection constraint from the 3D bounding box to the 2D box as an important component. Four edges of a 2D box provide only four constraints and the performance deteriorates dramatically with the small error of the 2D detector. Different from these approaches, our method predicts the nine perspective keypoints of a 3D bounding box in image space, and then utilize the geometric relationship of 3D and 2D perspectives to recover the dimension, location, and orientation in 3D space. In this method, the properties of the object can be predicted stably even when the estimation of keypoints is very noisy, which enables us to obtain fast detection speed with a small architecture. Training our method only uses the 3D properties of the object without any extra annotations, category-specific 3D shape priors, or depth maps. Our method is the first real-time system (FPS > 24) for monocular image 3D detection while achieves state-of-the-art performance on the KITTI benchmark. © 2020, Springer Nature Switzerland AG.

关键词： Real time systems

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Policy-Guided Lazy Search with Feedback for Task and Motion Planning

Policy-Guided Lazy Search with Feedback for Task and Motion ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Mohamed Khodeir Atharv Sonwane Ruthrash Hari Florian Shkurti Robot Vision and Learning Lab University of Toronto Robotics Institute Dept. of Computer Science University of BITS Pilani Dept. of Mathematical and Computational Sciences University of Toronto

PDDLStream solvers have recently emerged as viable solutions for Task and Motion Planning (TAMP) problems, extending PDDL to problems with continuous action spaces. Prior work has shown how PDDLStream problems can be reduced to a sequence of PDDL planning problems, which can then be solved using off-the-shelf planners. However, this approach can suffer from long runtimes. In this paper we propose LAZY, a solver for PDDLStream problems that maintains a single integrated search over action skeletons, which gets progressively more geometrically informed, as samples of possible motions are lazily drawn during motion planning. We explore how learned models of goal-directed policies and current motion sampling data can be incorporated in LAZY to adaptively guide the task planner. We show that this leads to significant speed-ups in the search for a feasible solution evaluated over unseen test environments of varying numbers of objects, goals, and initial conditions. We evaluate our TAMP approach by comparing to existing solvers for PDDLStream problems on a range of simulated 7DoF rearrangement/manipulation problems. Code can be found at https://***/learning-based-tamp.

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Towards robust monocular visual odometry for flying robots on planetary missions

arXiv

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arXiv 2021年

作者： Wudenka, Martin Müller, Marcus G. Demmel, Nikolaus Wedler, Armin Triebel, Rudolph Cremers, Daniel Stürzl, Wolfgang Institute of Robotics and Mechatronics German Aerospace Center DLR Computer Vision Group Department of Informatics Technical University of Munich Germany Autonomous Systems Lab ETH Zurich Switzerland

In the future, extraterrestrial expeditions will not only be conducted by rovers but also by flying robots. The technical demonstration drone Ingenuity, that just landed on Mars, will mark the beginning of a new era of exploration unhindered by terrain traversability. Robust self-localization is crucial for that. Cameras that are lightweight, cheap and information-rich sensors are already used to estimate the ego-motion of vehicles. However, methods proven to work in man-made environments cannot simply be deployed on other planets. The highly repetitive textures present in the wastelands of Mars pose a huge challenge to descriptor matching based approaches. In this paper, we present an advanced robust monocular odometry algorithm that uses efficient optical flow tracking to obtain feature correspondences between images and a refined keyframe selection criterion. In contrast to most other approaches, our framework can also handle rotation-only motions that are particularly challenging for monocular odometry systems. Furthermore, we present a novel approach to estimate the current risk of scale drift based on a principal component analysis of the relative translation information matrix. This way we obtain an implicit measure of uncertainty. We evaluate the validity of our approach on all sequences of a challenging real-world dataset captured in a Mars-like environment and show that it outperforms state-of-the-art approaches. The source code is publicly available at: https://***/DLR-RM/granite. Copyright © 2021, The Authors. All rights reserved.

关键词： Principal component analysis

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