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arXiv 2024年

作者： Shi, Hao Wang, Song Zhang, Jiaming Yin, Xiaoting Wang, Zhongdao Wang, Guangming Zhu, Jianke Yang, Kailun Wang, Kaiwei The State Key Laboratory of Extreme Photonics and Instrumentation The National Engineering Research Center of Optical Instrumentation Zhejiang University Hangzhou310027 China The School of Robotics The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China The College of Computer Science and Technology Zhejiang University Hangzhou310027 China The Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe76131 Germany Huawei Noah’s Ark Lab. Canada The Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom

Vision-based occupancy prediction, also known as 3D Semantic Scene Completion (SSC), presents a significant challenge in computer vision. Previous methods, confined to onboard processing, struggle with simultaneous geometric and semantic estimation, continuity across varying viewpoints, and single-view occlusion. Our paper introduces OccFiner, a novel offboard framework designed to enhance the accuracy of vision-based occupancy predictions. OccFiner operates in two hybrid phases: 1) a multi-to-multi local propagation network that implicitly aligns and processes multiple local frames for correcting onboard model errors and consistently enhancing occupancy accuracy across all distances. 2) the region-centric global propagation, focuses on refining labels using explicit multi-view geometry and integrating sensor bias, especially to increase the accuracy of distant occupied voxels. Extensive experiments demonstrate that OccFiner improves both geometric and semantic accuracy across various types of coarse occupancy, setting a new state-of-the-art performance on the SemanticKITTI dataset. Notably, OccFiner elevates vision-based SSC models to a level even surpassing that of LiDAR-based onboard SSC models. Furthermore, OccFiner is the first to achieve automatic annotation of SSC in a purely vision-based approach. Quantitative experiments prove that OccFiner successfully facilitates occupancy data loop-closure in autonomous driving. Additionally, we quantitatively and qualitatively validate the superiority of the offboard approach on city-level SSC static maps. The source code will be made publicly available at https://***/MasterHow/OccFiner. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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control of a Smart Walker for Training Using Interaction-Energy and Personalized Parameters ⁎

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IFAC-PapersOnLine 2020年第2期53卷 10154-10161页

作者： Denis Stogl Daniel Zumkeller Manuel Muth Björn Hein Institute for Anthropomatics and Robotics Intelligent Process Automation and Robotics Lab (IAR-IPR) Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Karlsruhe University of Applied Science Karlsruhe Germany

Smart walkers with admittance controller usually have limited dynamics and low maximal velocity to provide stable and safe behavior. To enable physically challenging training with smart walker control strategies enabling faster dynamics is needed. However, in certain situations, this can lead to instabilities, which further complicates the finding of suitable parameters for the envisioned training functionalities. To overcome these issues, we have introduced an interaction-energy limiter and developed a strategy to automatically determine individual user parameters for an adaptive admittance controller. The energy limiter bounds the controller and training elements to avoid uncomfortable and dangerous situations. These training elements are placed on a 2D map of a training environment. If the user passes these training elements with our smart walker - RoboTrainer - they are triggered. Therefore we call them spatial control actions. We can show that interaction-energy limiter successfully avoids instabilities and dangerous situations when spatial control actions are triggered. The evaluation with 22 users, which used Robo Trainer with and without individualized parameters, successfully demonstrate the benefits of the parameterization method. The presented method could be generally valuable for the implementation of smart walkers in everyday life since it provides a solution for dealing with users with different skills and a solution for safe interaction with smart walkers using high-dynamic control.

关键词： Human-machine interface Human-machine interaction Admittance control Model-based control Human-centered design Nonlinear gain Mobile robots

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TransKD: Transformer Knowledge Distillation for Efficient Semantic Segmentation

arXiv

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arXiv 2022年

作者： Liu, Ruiping Yang, Kailun Roitberg, Alina Zhang, Jiaming Peng, Kunyu Liu, Huayao Wang, Yaonan Stiefelhagen, Rainer The Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe76131 Germany The School of Robotics The National Engineering Laboratory of Robot Visual Perception and Control Technology Hunan University Changsha410082 China The Institute for Artificial Intelligence The University of Stuttgart Stuttgart70569 Germany The Institute for Visual Computing ETH Zurich Zurich8092 Switzerland NIO Shanghai201804 China

Semantic segmentation benchmarks in the realm of autonomous driving are dominated by large pre-trained transformers, yet their widespread adoption is impeded by substantial computational costs and prolonged training durations. To lift this constraint, we look at efficient semantic segmentation from a perspective of comprehensive knowledge distillation and aim to bridge the gap between multi-source knowledge extractions and transformer-specific patch embeddings. We put forward the Transformer-based Knowledge Distillation (TransKD) framework which learns compact student transformers by distilling both feature maps and patch embeddings of large teacher transformers, bypassing the long pre-training process and reducing the FLOPs by >85.0%. Specifically, we propose two fundamental modules to realize feature map distillation and patch embedding distillation, respectively: (1) Cross Selective Fusion (CSF) enables knowledge transfer between cross-stage features via channel attention and feature map distillation within hierarchical transformers;(2) Patch Embedding Alignment (PEA) performs dimensional transformation within the patchifying process to facilitate the patch embedding distillation. Furthermore, we introduce two optimization modules to enhance the patch embedding distillation from different perspectives: (1) Global-Local Context Mixer (GL-Mixer) extracts both global and local information of a representative embedding;(2) Embedding Assistant (EA) acts as an embedding method to seamlessly bridge teacher and student models with the teacher’s number of channels. Experiments on Cityscapes, ACDC, NYUv2, and Pascal VOC2012 datasets show that TransKD outperforms state-of-the-art distillation frameworks and rivals the time-consuming pre-training method. The source code is publicly available at https://***/RuipingL/TransKD. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantic Segmentation

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DTCLMapper: Dual Temporal Consistent Learning for Vectorized HD Map Construction

arXiv

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arXiv 2024年

作者： Li, Siyu Lin, Jiacheng Shi, Hao Zhang, Jiaming Wang, Song Yao, You Li, Zhiyong Yang, Kailun The School of Robotics The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China The College of Computer Science and Electronic Engineering Hunan University Changsha410082 China The State Key Laboratory of Extreme Photonics and Instrumentation The National Engineering Research Center of Optical Instrumentation Zhejiang University Hangzhou310027 China The Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe76131 Germany The College of Computer Science Zhejiang University Hangzhou310027 China The USC Viterbi School of Engineering The University of Southern California Los AngelesCA90089 United States

Temporal information plays a pivotal role in Bird’s-Eye-View (BEV) driving scene understanding, which can alleviate the visual information sparsity. However, the indiscriminate temporal fusion method will cause the barrier of feature redundancy when constructing vectorized High-Definition (HD) maps. In this paper, we revisit the temporal fusion of vectorized HD maps, focusing on temporal instance consistency and temporal map consistency learning. To improve the representation of instances in single-frame maps, we introduce a novel method, DTCLMapper. This approach uses a dual-stream temporal consistency learning module that combines instance embedding with geometry maps. In the instance embedding component, our approach integrates temporal Instance Consistency Learning (ICL), ensuring consistency from vector points and instance features aggregated from points. A vectorized points pre-selection module is employed to enhance the regression efficiency of vector points from each instance. Then aggregated instance features obtained from the vectorized points preselection module are grounded in contrastive learning to realize temporal consistency, where positive and negative samples are selected based on position and semantic information. The geometry mapping component introduces Map Consistency Learning (MCL) designed with self-supervised learning. The MCL enhances the generalization capability of our consistent learning approach by concentrating on the global location and distribution constraints of the instances. Extensive experiments on well-recognized benchmarks indicate that the proposed DTCLMapper achieves state-of-the-art performance in vectorized mapping tasks, reaching 61.9% and 65.1% mAP scores on the nuScenes and Argoverse datasets, respectively. The source code is available at https://***/lynn-yu/DTCLMapper. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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CoBEV: Elevating Roadside 3D Object Detection with Depth and Height Complementarity

arXiv

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arXiv 2023年

作者： Shi, Hao Pang, Chengshan Zhang, Jiaming Yang, Kailun Wu, Yuhao Ni, Huajian Lin, Yining Stiefelhagen, Rainer Wang, Kaiwei The State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang University Hangzhou310027 China The Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe76131 Germany The Department of Engineering Science University of Oxford OxfordOX1 3PJ United Kingdom The School of Robotics Hunan University Changsha410012 China The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China Shanghai SUPREMIND Technology Co. Ltd. Shanghai201210 China

Roadside camera-driven 3D object detection is a crucial task in intelligent transportation systems, which extends the perception range beyond the limitations of vision-centric vehicles and enhances road safety. While previous studies have limitations in using only depth or height information, we find both depth and height matter and they are in fact complementary. The depth feature encompasses precise geometric cues, whereas the height feature is primarily focused on distinguishing between various categories of height intervals, essentially providing semantic context. This insight motivates the development of Complementary-BEV (CoBEV), a novel end-to-end monocular 3D object detection framework that integrates depth and height to construct robust BEV representations. In essence, CoBEV estimates each pixel's depth and height distribution and lifts the camera features into 3D space for lateral fusion using the newly proposed two-stage complementary feature selection (CFS) module. A BEV feature distillation framework is also seamlessly integrated to further enhance the detection accuracy from the prior knowledge of the fusion-modal CoBEV teacher. We conduct extensive experiments on the public 3D detection benchmarks of roadside camera-based DAIR-V2X-I and Rope3D, as well as the private Supremind-Road dataset, demonstrating that CoBEV not only achieves the accuracy of the new state-of-the-art, but also significantly advances the robustness of previous methods in challenging long-distance scenarios and noisy camera disturbance, and enhances generalization by a large margin in heterologous settings with drastic changes in scene and camera parameters. For the first time, the vehicle AP score of a camera model reaches 80% on DAIR-V2X-I in terms of easy mode. The source code will be made publicly available at CoBEV. Copyright © 2023, The Authors. All rights reserved.

关键词： Feature Selection

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A Unified Perception Benchmark for Capacitive Proximity Sensing Towards Safe Human-Robot Collaboration (HRC)

A Unified Perception Benchmark for Capacitive Proximity Sens...

引用

IEEE International Conference on robotics and Automation (ICRA)

作者： Serkan Ergun Yitao Ding Hosam Alagi Christian Schöffmann Barnaba Ubezio Gergely Soti Michael Rathmair Stephan Mühlbacher-Karrer Ulrike Thomas Björn Hein Michael Hofbaur Hubert Zangl Institute of Smart Systems Technologies Klagenfurt University (AAU) Klagenfurt am Wörthersee Austria Lab of Robotics and Human-Machine-Interaction Chemnitz University of Technology (TUC) Chemnitz Germany Institute for Anthropomatics and Robotics - Intelligent Process Automation and Robotics Lab (IAR - IPR) Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Joanneum Research Robotics (Jr) Institute of Robotics and Mechatronics Klagenfurt am Wörthersee Austria Karlsruhe University of Applied Sciences

During the co-presence of human workers and robots, measures are required to avoid injuries from undesired contacts. Capacitive Proximity Sensors (CPSs) offer a cost-effective solution to cover the entire robot manipulator with fast close-range perception for HRC tasks, closing the perception gap between tactile detection and mid-range perception. CPSs do not suffer from occlusion and compared to pure tactile or force sensing, they react earlier and allow increasing the operating speed of Collaborative Robots (Cobots) while still maintaining safety. However, since capacitive coupling to obstacles varies with their distance, shape and material properties, the projection from capacitance to actual distances is a general problem. In this work, we propose an universal benchmark test procedure for fellow researchers to evaluate their CPSs. Considering ISO/TS 15066 for Power and Force Limiting (PFL) as a reference, we derive the requirements for the specified body regions and propose a method for determining the operation speed to comply with PFL based on a pre-defined detection threshold. Finally, the benchmark test procedure is evaluated on three different concepts of CPSs from the contributed researchers, demonstrating the general applicability.

关键词： Shape Force Collaboration Benchmark testing Robot sensing systems Manipulators Sensors

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LF-VIO: A Visual-Inertial-Odometry Framework for Large Field-of-View Cameras with Negative Plane

arXiv

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arXiv 2022年

作者： Wang, Ze Yang, Kailun Shi, Hao Li, Peng Gao, Fei Wang, Kaiwei State Key Laboratory of Modern Optical Instrumentation Zhejiang University China Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Germany State Key Laboratory of Industrial Control Technology Zhejiang University China Huzhou Institute of Zhejiang University Zhejiang University China

Visual-inertial-odometry has attracted extensive attention in the field of autonomous driving and robotics. The size of Field of View (FoV) plays an important role in Visual-Odometry (VO) and Visual-Inertial-Odometry (VIO), as a large FoV enables to perceive a wide range of surrounding scene elements and features. However, when the field of the camera reaches the negative half plane, one cannot simply use [u, v, 1]T to represent the image feature points anymore. To tackle this issue, we propose LF-VIO, a real-time VIO framework for cameras with extremely large FoV. We leverage a three-dimensional vector with unit length to represent feature points, and design a series of algorithms to overcome this challenge. To address the scarcity of panoramic visual odometry datasets with ground-truth location and pose, we present the PALVIO dataset, collected with a Panoramic Annular Lens (PAL) system with an entire FoV of 360◦×(40◦∼120◦) and an IMU sensor. With a comprehensive variety of experiments, the proposed LF-VIO is verified on both the established PALVIO benchmark and a public fisheye camera dataset with a FoV of 360◦×(0◦∼93.5◦). LF-VIO outperforms state-of-the-art visual-inertial-odometry methods. Our dataset and code are made publicly available at https://***/flysoaryun/LF-VIO. Copyright © 2022, The Authors. All rights reserved.

关键词： Cameras

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Monitoring the Evolution of Dimensional Accuracy and Product Properties in Property-controlled Forming processes

SSRN

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SSRN 2023年

作者： Stebner, Sophie Charlotte Martschin, Juri Arian, Bahman Dietrich, Stefan Feistle, Martin Hütter, Sebastian Lafarge, Rémi Laue, Robert Li, Xinyiang Schulte, Christopher Spies, Daniel Thein, Ferdinand Wendler, Frank Wrobel, Malte Vasquez, Julian Rozo Dölz, Michael Münstermann, Sebastian Integrity of Materials and Structures RWTH Aachen University Aachen Germany Institute of Forming Technology and Lightweight Components Dortmund University Dortmund Germany Forming and Machining Technology Paderborn University Paderborn Germany Institute for Applied Materials - Material Science Karlsruhe Institute of Technology Karlsruhe Germany Fraunhofer Institute for Casting Composite and Processing Technology Garching Germany Institute of Materials and Joining Technology Otto-von-Guericke University Magdeburg Germany Forming and Machining Processes Technische Universität Dresden Dresden Germany Institute for Machine Tools and Productions Processes Virtual Production Engineering Chemnitz University of Technology Chemnitz Germany Institute of Metal Forming RWTH Aachen University Aachen Germany Institute of Automatic Control RWTH Aachen University Aachen Germany Institute for Production Engineering and Forming Machines Technical University of Darmstadt Darmstadt Germany Institut für Geometrie und Praktische Mathematik RWTH Aachen University Aachen Germany Measurement and Sensor Technology Chemnitz University of Technology Chemnitz Germany Digital Process Engineering Group Institute for Mechanical Process Engineering and Mechanics Karlsruhe Institute of Technology Karlsruhe Germany Materials Test Engineering Dortmund University Dortmund Germany

As recent trends in manufacturing engineering disciplines show a clear development in the sustainable as well as economically efficient design of forming processes, monitoring techniques have been gaining in relevance. In terms of monitoring of product properties, most processes are currently open-loop controlled, entailing that the microstructure evolution, which determines the final product properties, is not considered. However, a closed-loop control that can adjust and manipulate the process actuators according to the required product properties of the component will lead to a considerable increase in efficiency of the processes regarding resources and will decrease postproduction of the component. For most forming processes, one set of component dimensions will result in a certain set of product properties. However, to successfully establish closed-loop property controls for the processes, a systematic understanding of the reciprocity of the dimensions after forming and final product properties must be established. This work investigates the evolution of dimensional accuracy as well as product properties for a series of forming processes that utilize different degrees of freedom for process control. © 2023, The Authors. All rights reserved.

关键词： process control

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Computational Imaging for Machine Perception: Transferring Semantic Segmentation beyond Aberrations

arXiv

引用

arXiv 2022年

作者： Jiang, Qi Shi, Hao Gao, Shaohua Zhang, Jiaming Yang, Kailun Sun, Lei Ni, Huajian Wang, Kaiwei The State Key Laboratory of Extreme Photonics and Instrumentation The National Engineering Research Center of Optical Instrumentation Zhejiang University Hangzhou310027 China The Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe76131 Germany The School of Robotics The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China Shanghai SUPREMIND Technology Company Ltd Shanghai201210 China

Semantic scene understanding with Minimalist Optical Systems (MOS) in mobile and wearable applications remains a challenge due to the corrupted imaging quality induced by optical aberrations. However, previous works only focus on improving the subjective imaging quality through the Computational Imaging (CI) technique, ignoring the feasibility of advancing semantic segmentation. In this paper, we pioneer the investigation of Semantic Segmentation under Optical Aberrations (SSOA) with MOS. To benchmark SSOA, we construct Virtual Prototype Lens (VPL) groups through optical simulation, generating Cityscapes-ab and KITTI-360-ab datasets under different behaviors and levels of aberrations. We look into SSOA via an unsupervised domain adaptation perspective to address the scarcity of labeled aberration data in real-world scenarios. Further, we propose Computational Imaging Assisted Domain Adaptation (CIADA) to leverage prior knowledge of CI for robust performance in SSOA. Based on our benchmark, we conduct experiments on the robustness of classical segmenters against aberrations. In addition, extensive evaluations of possible solutions to SSOA reveal that CIADA achieves superior performance under all aberration distributions, bridging the gap between computational imaging and downstream applications for MOS. The project page is at https://***/zju-jiangqi/CIADA. Copyright © 2022, The Authors. All rights reserved.

关键词： Aberrations

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On Optimal control of Flat Hybrid Automata

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IFAC-PapersOnLine 2020年第2期53卷 6800-6805页

作者： Frederik Zahn Tobias Kleinert Veit Hagenmeyer Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany Chair of Process Control Engineering RWTH Aachen University Aachen Germany

Recently, Flat Hybrid Automata (FHA) were introduced as a new model class of hybrid systems. In order to answer the evident question of an optimal operation of the new class of FHA, a well-posed optimization problem definition for FHA is presented. This problem formulation also includes costs on discrete–state transitions and switching actions. We present a solution for a reduced problem: FHA with autonomous switching. For these, a new algorithm is introduced which solves the optimal control problem. The application of this optimization algorithm is shown with an electrical network example.

关键词： Flat hybrid automata differential flatness hybrid switched systems modeling optimal control of hybrid systems

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