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20th International Conference on Advanced robotics, ICAR 2021

作者： Popovic, Goran Cviic, Igor Markovic, Ivan Petrovic, Ivan Laboratory For Autonomous Systems and Mobile Robotics University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia

ISBN: (纸本)9781665436847

Modern warehouses, equipped with an autonomous robots fleet, suffer from an efficiency deficit during human interventions in the shop floor area, since, during such interventions, all the robots must stop for safety reasons. This particularly affects large warehouses which could extremely benefit from a solution that would allow human-robot collaboration during such interventions. This solution should only stop the robots that are in the vicinity of the human and reroute the others, and it requires the following prerequisites: (i) reliable relative ranging of robots with respect to human workers that would ensure safety, and (ii) reliable localization of human workers in the warehouse. In this paper we propose a system for reliable human localization that is based on fusing cues from measured relative ranges with respect to moving robots and a camera system that is worn by the human worker. Since global locations of the robots in the warehouse are known, we use the relative ranges to robots in an indoor GPS-like fashion to provide global human location updates, while the wearable cameras provide visual odometry, i.e., the relative motion of the human, and another global cue by detecting ground fiducial markers used by robots. The fusion is performed within a graph-based optimization framework that also includes a visual odometry error model. We carried out an experimental analysis on three different datasets to validate the performance of the proposed approach and compared it to a state-of-the-art visual SLAM solution, namely ORB-SLAM2. The results demonstrate that our approach yields reliable human location within warehouse like environments. © 2021 IEEE.

关键词： Warehouses

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Stereo Visual Localization Dataset Featuring Event Cameras

Stereo Visual Localization Dataset Featuring Event Cameras

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European Conference on Mobile Robots (ECMR)

作者： Antea Hadviger Vlaho-Josip Štironja Igor Cvišić Ivan Marković Sacha Vražić Ivan Petrović Laboratory for Autonomous Systems and Mobile Robotics University of Zagreb Faculty of Electrical Engineering and Computing Croatia Rimac Automobili Croatia

Visual odometry and SLAM methods are facing increasingly complex scenarios and novel solutions are needed to offer more accurate and reliable results in challenging environments. Standard cameras are challenged under low light conditions or very high-speed motion, as they suffer from motion blur and operate at a limited frame rate. These problems can be alleviated by using event cameras - asynchronous visual sensors that offer complementary advantages compared to standard cameras, as they do not suffer from motion blur and support high dynamic range. Although there are a number of existing datasets intended for visual odometry and SLAM that contain event data, most of them are collected using monocular sensors and limited either in terms of camera resolution or ground truth availability. Our work aims to complement this by further supporting the development of robust stereo visual odometry and SLAM algorithms, allowing to exploit both event data and intensity images. We provide both indoor sequences with 6-DoF motion and outdoor vehicle driving sequences that additionally contain 3D lidar data. All sequences contain data from a synchronized high-resolution stereo event and standard cameras, whereas ground truth trajectories are provided by either a motion capture system or a highly accurate GNSS/INS and AHRS that combines the fibre-optic gyro IMU with a dual antenna RTK GNSS receiver.

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Transparent Objects: A Corner Case in Stereo Matching

Transparent Objects: A Corner Case in Stereo Matching

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IEEE International Conference on robotics and Automation (ICRA)

作者： Zhiyuan Wu Shuai Su Qijun Chen Rui Fan The Robotics & Artificial Intelligence Laboratory (RAIL) the State Key Laboratory of Intelligent Autonomous Systems Machine Intelligence & Autonomous Systems (MIAS) Group the College of Electronic & Information Engineering Tongji University Frontiers Science Center for Intelligent Autonomous Systems Shanghai P. R. China

Stereo matching is a common technique used in 3D perception, but transparent objects such as reflective and penetrable glass pose a challenge as their disparities are often estimated inaccurately. In this paper, we propose transparency-aware stereo (TA-Stereo), an effective solution to tackle this issue. TA-Stereo first utilizes a semantic segmentation or salient object detection network to identify transparent objects, and then homogenizes them to enable stereo matching algorithms to handle them as non-transparent objects. To validate the effectiveness of our proposed TA-Stereo strategy, we collect 260 images containing transparent objects from the KITTI Stereo 2012 and 2015 datasets and manually label pixel-level ground truth. We evaluate our strategy with six deep stereo networks and two types of transparent object detection methods. Our experiments demonstrate that TA-Stereo significantly improves the disparity accuracy of transparent objects. Our project webpage can be accessed at ***/TA-Stereo.

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Genetic Algorithms for Optimal Grasping in Automated 3D-Printed Part Extraction

Genetic Algorithms for Optimal Grasping in Automated 3D-Prin...

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Iberian robotics Conference (ROBOT)

作者： Enrique García Torija Virgilió Gomez Lambo Mohamed Khalil Kahlaoui David Álvarez Miguel Hernando Ricardo Sanz Centre for Automation and Robotics Universidad Politécnica de Madrid Madrid Spain Autonomous Systems Laboratory Universidad Politécnica de Madrid Madrid Spain

ISBN: (数字)9798350376364

ISBN: (纸本)9798350376371

This article presents a novel approach to automate the extraction of 3D printed parts using genetic algorithms for optimal grasp planning. The method addresses a key challenge in additive manufacturing: the automated removal of printed objects. Using information from the G-code file, our algorithm determines the most effective grasping pose for a robotic arm equipped with a two-finger gripper. The system considers multiple factors including collision avoidance, contact quality, and part geometry to optimize the grasp location. The solution was implemented using an industrial manipulator and integrated with a commercial 3D printer through a custom ROS 2 package. Experimental results demonstrate the effectiveness of our approach on simple geometries, successfully extracting printed parts without human intervention. While the method has certain limitations, this study lays the foundation for fully autonomous 3D printing workflows. Our research contributes to the broader goal of developing adaptive and self-sufficient manufacturing systems, paving the way for more efficient and flexible production processes in industry 4.0 environments.

关键词： Geometry Service robots Grasping Production Three-dimensional printing Manipulators Printers Collision avoidance Grippers Genetic algorithms

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Extrinsic Calibration of 2D Millimetre-Wavelength Radar Pairs Using Ego-Velocity Estimates

Extrinsic Calibration of 2D Millimetre-Wavelength Radar Pair...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Qilong Cheng Emmett Wise Jonathan Kelly Space & Terrestrial Autonomous Robotics Systems Laboratory University of Toronto Institute for Aerospace Studies Toronto Canada Vector Institute Faculty

Correct radar data fusion depends on knowledge of the spatial transform between sensor pairs. Current methods for determining this transform operate by aligning identifiable features in different radar scans, or by relying on measurements from another, more accurate sensor. Feature-based alignment requires the sensors to have overlapping fields of view or necessitates the construction of an environment map. Several existing techniques require bespoke retroreflective radar targets. These requirements limit both where and how calibration can be performed. In this paper, we take a different approach: instead of attempting to track targets or features, we rely on ego-velocity estimates from each radar to perform calibration. Our method enables calibration of a subset of the transform parameters, including the yaw and the axis of translation between the radar pair, without the need for a shared field of view or for specialized targets. In general, the yaw and the axis of translation are the most important parameters for data fusion, the most likely to vary over time, and the most difficult to calibrate manually. We formulate calibration as a batch optimization problem, show that the radar-radar system is identifiable, and specify the platform excitation requirements. Through simulation studies and real-world experiments, we establish that our method is more reliable and accurate than state-of-the-art methods. Finally, we demonstrate that the full rigid body transform can be recovered if relatively coarse information about the platform rotation rate is available.

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TReCiM: Lower Power and Temperature-Resilient Multibit 2FeFET-1T Compute-in-Memory Design 24

TReCiM: Lower Power and Temperature-Resilient Multibit 2FeFE...

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43rd International Conference on Computer-Aided Design, ICCAD 2024

作者： Zhou, Yifei Kämpfe, Thomas Ni, Kai Amrouch, Hussam Zhuo, Cheng Yin, Xunzhao Zhejiang University Hangzhou China Fraunhofer IPMS Dresden Germany Department of Electrical Engineering University of Notre Dame Notre Dame United States Technical University of Munich TUM School of Computation Information and Technology Munich Institute of Robotics and Machine Intelligence Munich Germany Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province China

ISBN: (纸本)9798400710773

Compute-in-memory (CiM) emerges as a promising solution to solve hardware challenges in artificial intelligence (AI) and the Internet of Things (IoT), particularly addressing the"memory wall" issue. By utilizing nonvolatile memory (NVM) devices in a crossbar structure, CiM efficiently accelerates multiply-accumulate (MAC) computations, the crucial operations in neural networks and other AI models. Among various NVM devices, Ferroelectric FET (FeFET) is particularly appealing for ultra-low-power CiM arrays due to its CMOS compatibility, voltage-driven write/read mechanisms and high ION/IOFF ratio. Moreover, subthreshold-operated FeFETs, which operate at scaling voltages in the subthreshold region, can further minimize the power consumption of CiM array. However, subthreshold-FeFETs are susceptible to temperature drift, resulting in computation accuracy degradation. Existing solutions exhibit weak temperature resilience at larger array size and only support 1-bit. In this paper, we propose TReCiM, an ultra-low-power temperature-resilient multibit 2FeFET-1T CiM design that reliably performs MAC operations in the subthreshold-FeFET region with temperature ranging from 0°C to 85°C at scale. We benchmark our design using NeuroSim framework in the context of VGG-8 neural network architecture running the CIFAR-10 dataset. Benchmarking results suggest that when considering temperature drift impact, our proposed TReCiM array achieves 91.31% accuracy, with 1.86% accuracy improvement compared to existing 1-bit 2T-1FeFET CiM array. Furthermore, our proposed design achieves 48.03 TOPS/W energy efficiency at system level, comparable to existing designs with smaller technology feature sizes. © 2024 Copyright is held by the owner/author(s).

关键词： MISFET devices

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RAVE: A Framework for Radar Ego-Velocity Estimation

RAVE: A Framework for Radar Ego-Velocity Estimation

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International Conference on Multisensor Fusion and Integration for Intelligent systems (MFI)

作者： Vlaho-Josip Štironja Luka Petrović Juraj Peršić Ivan Marković Ivan Petrović Laboratory for Autonomous Systems and Mobile Robotics (LAMOR) University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia Calirad d.o.o. Zagreb Croatia

ISBN: (数字)9798350368031

ISBN: (纸本)9798350368048

State estimation is an essential component of autonomous systems, usually relying on sensor fusion that integrates data from cameras, LiDARs and IMUs. Recently, radars have shown the potential to improve the accuracy and robustness of state estimation and perception, especially in challenging environmental conditions such as adverse weather and low-light scenarios. In this paper, we present a framework for ego-velocity estimation, which we call RAVE, that relies on 3D automotive radar data and encompasses zero velocity detection, outlier rejection, and velocity estimation. In addition, we propose a simple filtering method to discard infeasible egovelocity estimates. We also conduct a systematic analysis of how different existing outlier rejection techniques and optimization loss functions impact estimation accuracy. Our evaluation on three open-source datasets demonstrates the effectiveness of the proposed filter and a significant positive impact of RAVE on the odometry accuracy. Furthermore, we release an opensource implementation of the proposed framework for radar ego-velocity estimation accompanied with a ROS interface.

关键词： Accuracy Three-dimensional displays Systematics Filtering Radar Trajectory Odometry State estimation Optimization Automotive engineering

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Multi-Object Graph Affordance Network: Goal-Oriented Planning through Learned Compound Object Affordances

arXiv

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arXiv 2023年

作者： Girgin, Tuba Ugur, Emre Department of Computer Engineering Bogazici University Istanbul Turkey Robotics and Autonomous Systems Laboratory TUBITAK BIL-GEM Kocaeli41470 Turkey

—Learning object affordances is an effective tool in the field of robot learning. While the data-driven models investigate affordances of single or paired objects, there is a gap in the exploration of affordances of compound objects composed of an arbitrary number of objects. We propose the Multi-Object Graph Affordance Network which models complex compound object affordances by learning the outcomes of robot actions that facilitate interactions between an object and a compound. Given the depth images of the objects, the object features are extracted via convolution operations and encoded in the nodes of graph neural networks. Graph convolution operations are used to encode the state of the compounds, which are used as input to decoders to predict the outcome of the object-compound interactions. After learning the compound object affordances, given different tasks, the learned outcome predictors are used to plan sequences of stack actions that involve stacking objects on top of each other, inserting smaller objects into larger containers and passing through ring-like objects through poles. We showed that our system successfully modeled the affordances of compound objects that include concave and convex objects, in both simulated and real-world environments. We benchmarked our system with a baseline model to highlight its advantages. © 2023, CC BY.

关键词： Convolution

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Cloud Assisted Connected and Automated Mobility System Architecture Design and Experimental Verification: The 5G-MOBIX autonomous Truck Routing Use Case

Cloud Assisted Connected and Automated Mobility System Archi...

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IEEE Symposium on Intelligent Vehicle

作者： Tahir Sari Mert Sever Arda Taha Candan Gü lsü m Tuba Ç ibuk Girgin Emre Girgin Mehmet Haklı dı r Research and Development Center Autonomous Driving Systems Ford Otosan Istanbul Turkey Robotics and Autonomous Systems Laboratory TUBITAK BILGEM Kocaeli Turkey

In this study, potential usage of 5G networks for cloud assisted connected and autonomous mobility solution is demonstrated with preliminary field tests of autonomous Truck Routing use case in H2020-ICT18 5G-MOBIX project. The preliminary tests are performed at Ford Otosan test track in Eskişehir, Turkey. The SAFIR cloud server developed by TUBITAK BILGEM is used over 5G network. The 5G-MOBIX autonomous Truck Routing system architecture is composed of an autonomous truck, cloud and smart infrastructure having three road side units with LIDAR sensors. Navigation software stack is operated on cloud according to data received from autonomous truck and road side units. On-board unit performs required data transmission between cloud and truck. Motion control algorithms are executed to follow reference way points with a rapid prototyping controller in autonomous truck. Feasibility of the proposed connected and automated mobility system architecture is verified.

关键词： 5G mobile communication Road side unit Software algorithms systems architecture Routing Path planning Software

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Auto-Augmentation Contrastive Learning for Wearable-based Human Activity Recognition

Auto-Augmentation Contrastive Learning for Wearable-based Hu...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wu, Qingyu Shen, Jianfei Fan, Feiyi Gu, Yang Xu, Chenyang Chen, Yiqiang Chinese Academy of Sciences Institute of Computing Technology Beijing China University of Chinese Academy of Sciences Beijing China The Beijing Key Laboratory of Mobile Computing and Pervasive Device Beijing China Shandong Academy of Intelligent Computing Technology ShanDong China Peng Cheng Laboratory Shenzhen China Tianjin University Institute of Robotics and Autonomous Systems Tianjin China

ISBN: (纸本)9798350337488

For low-semantic sensor signals from human activity recognition (HAR), contrastive learning (CL) is essential to implement novel applications or generic models without manual annotation, which is a high-performance self-supervised learning (SSL) method. However, CL relies heavily on data augmentation for pairwise comparisons. Especially for low semantic data in the HAR area, conducting good performance augmentation strategies in pretext tasks still rely on manual attempts lacking generalizability and flexibility. To reduce the augmentation burden, we propose an end-to-end auto-augmentation contrastive learning (AutoCL) method for wearable-based HAR. AutoCL is based on a Siamese network architecture that shares the parameters of the backbone and with a generator embedded to learn auto-augmentation. AutoCL trains the generator based on the representation in the latent space to overcome the disturbances caused by noise and redundant information in raw sensor data. The architecture empirical study indicates the effectiveness of this design. Furthermore, we propose a stop-gradient design and correlation reduction strategy in AutoCL to enhance encoder representation learning. Extensive experiments based on four wide-used HAR datasets demonstrate that the proposed AutoCL method significantly improves recognition accuracy compared with other SOTA methods. © 2023 IEEE.

关键词： Contrastive Learning Human Activity Recognition Self-Supervised Learning

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