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SVM-based Extended Kalman Filter State Estimation of Biped Robot

SVM-based Extended Kalman Filter State Estimation of Biped R...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Gao, Chengzhi Xie, Ye Zhu, Shiqiang Huang, Guanyu Kong, Lingyu Xie, Anhuan Gu, Jianjun Zhang, Dan Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Zhejiang University Zhejiang Hangzhou311100 China York University North YorkM3J 1P3 Canada Department of Electrical and Computer Engineering Dalhousie University HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9781665481090

At present, more and more attention has been paid to the exploration of biped robots. As the precondition of biped stable walking, the state estimation of robot has also received the attention of many experts. Biped robot has special motion characteristics, has a hybrid dynamic system and belongs to a floating base, so it must obtain the contact information of two feet before realizing the motion state estimation of the robot. The contribution of this paper is to introduce soft support vector machine to detect contact phase. This method based on support vector machine (SVM) eliminates the need for force sensors on two feet because they are vulnerable to the impact between the foot and the contact surface. In addition, precise dynamic modeling is not required. Furthermore, this paper uses SVM to obtain the touchdown information, and proposes a state estimation method using extended Kalman filter to combine motion estimation with IMU data. Finally, the touchdown detection algorithm and the state estimation algorithm are verified through experiments, and the accuracy and feasibility of the SVM method are proved through the touchdown detection experiments. Based on this, the full state estimation of the robot is realized through the state estimation experiments. © 2022 IEEE.

关键词： State estimation

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Advancing Orchard Fruit Detection: An Innovative Agricultural Foundation Model Approach

Advancing Orchard Fruit Detection: An Innovative Agricultura...

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2024 American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2024

作者： Li, Jiajia Lammers, Kyle Yin, Xunyuan Yin, Xiang He, Long Lu, Renfu Li, Zhaojian Department of Electrical and Computer Engineering Michigan State University MI United States School of Chemical and Biomedical Engineering Nanyang Technological University Singapore Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China Department of Agricultural and Biological Engineering Pennsylvania State University United States United States Department of Agriculture Agricultural Research Service East LansingMI United States Department of Mechanical Engineering Michigan State University East LansingMI United States

ISBN: (纸本)9798331302214

Fruit harvesting poses a significant labor and financial burden on the fruit industry, which underscore the urgent need for advancements in robotic harvesting solutions. Despite considerable progress in leveraging deep learning and machine learning techniques for fruit detection, a common shortfall is the inability to swiftly extend the developed models across different orchards and/or various fruit species. Additionally, the limited availability of pertinent data further compounds these challenges. In this work, we introduce MetaFruit, the largest publicly available multi-class fruit dataset, comprising 4,248 images and 248,015 manually labeled instances across diverse U.S. orchards. Furthermore, this study proposes an innovative open-set fruit detection system leveraging advanced vision Foundation Models (VFMs) for fruit detection that can adeptly identify a wide array of fruit types under varying orchard conditions. This system not only demonstrates remarkable adaptability in learning from minimal data through few-shot learning but also shows the ability to interpret human instructions for subtle detection tasks. The performance of the developed foundation model is comprehensively evaluated across several metrics, outperforming existing state-of-the-art algorithms in our MetaFruit, thereby setting a new benchmark in the field of agricultural technology and robotic harvesting. The MetaFruit dataset (https://***/datasets/jiajiali/metafruit) and detection framework (https://***/JiajiaLi04/FMFruit) are open-sourced to foster future research in vision-based fruit harvesting, marking a significant stride toward addressing the urgent needs of the agricultural sector. © 2024 ASABE Annual International Meeting. All rights reserved.

关键词： Orchards

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Development of a compact mouse retinal imaging system using spectral domain optical coherence tomography (SD-OCT) with a full field stimulation channel for recording fast optoretinograms (ORGs) 34

Development of a compact mouse retinal imaging system using ...

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Ophthalmic Technologies XXXIV 2024

作者： Jo, Hang Chan Pijewska, Ewelina Meleppat, Ratheesh K. Jonnal, Ravi S. Kim, Dae Yu Zawadzki, Robert J. Department of Electrical and Computer Engineering Inha University 100 Inha-ro Michuhol-gu Incheon22212 Korea Republic of Dept. of Ophthalmology and Vision Science UC Davis Eye Center SacramentoCA95817 United States UC Davis Eye-Pod Small Animal Ocular Imaging Laboratory Department of Cell Biology and Human Anatomy University of California Davis 4320 Tupper Hall DavisCA95616 United States

ISBN: (纸本)9781510669079

While the a-wave of mouse electroretinogram (ERG) occurs within 50 milliseconds after exposure to light, the optoretinogram (ORG) slower than a 20Hz sampling rate could face limitations in observing immediate morphological changes from the retina. In this study, we develop a compact custom-built mouse ORG system based on spectral domain optical coherence tomography (SD-OCT) for 100Hz~1KHz B-scan rates comprised of 100 kHz A-scans. All the optics of the developed ORG system are designed on a 24 x 24 inches optical breadboard to move easily as well as to combine with the ERG system in a dark room. Without using a fundus camera, the OCT system provides en-face images from high-pass filtering and square of the OCT spectral signal for mouse retinal positioning in-vivo before acquiring ORG data. The 490nm LED for light stimulus is generated to make uniform illumination at the mouse retina using the Maxwellian view method. The common path of the OCT scanning light and the visible LED is built with achromatic doublet lens combinations based on optical simulation with Opticstudio® . The developed compact ORG system can not only observe light-evoked responses with 1~10 milliseconds but also be used for the studies of correlations between ORG and ERG in the mouse retina. © 2024 SPIE.

关键词： Optical tomography

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FEIF: Feature Excitation and Interactive Fusion for 6D Object Pose Estimation

FEIF: Feature Excitation and Interactive Fusion for 6D Objec...

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IEEE International Conference on robotics and Biomimetics

作者： Qiwei Meng Shiqiang Zhu Jianjun Gu Zheyuan Lin Ling Zhong Wei Song Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

ISBN: (纸本)9781665481106

In this paper, we present FEIF, a novel framework for 6D object pose estimation from a single RGBD image. Compared with previous approaches, this model fully leverages the complementary RGB and depth information through lay-erwise feature excitation and interactive fusion, thus improving the effectiveness and robustness of extracted features. The representative RGBD features can be subsequently applied for object keypoints prediction and pose computation. In addition, our FEIF includes a self-evaluation module to make robots aware of the confidence of pose estimation, so they are flexible to take the corresponding strategy for following tasks. Experiment results prove the SOTA performance of our model on LineMOD, Occlusion-LineMOD and YCB-Video datasets, and we also demonstrate its applicability and robustness in real-world robotic manipulation.

关键词： Technological innovation Computational modeling Biomimetics Pose estimation Feature extraction Robustness Data mining

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Low-cost Mars Terrain Classification System Based on Coarse-grained Annotation

Low-cost Mars Terrain Classification System Based on Coarse-...

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IEEE International Conference on robotics and Biomimetics

作者： Jian Zhang Yeheng Chen Shiqiang Zhu Yuehua Li Tian Xie Jason Gu Zhejiang Laboratory Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

ISBN: (纸本)9781665481106

Due to communication latency with remote ground sites, automatic recognition of Mars terrain is essential for the path-planning of rovers. Currently, most vision-based terrain classification require thousands of fine-grained training samples, while the undefined terrains on Mars are difficult to be classified or fine-grained labeled. Actually, most of the terrain categories can only be coarse-grained labeled due to several limitations, such as overlapped sub-regions, blurred borders, etc. To solve this problem, CACMT (Coarse-grained Annotation-based Classification for Mars Terrain) is proposed to generate the global fine-grained classification map from the local coarse-grained data. Specifically, the complete pipeline is decomposed into (i) annotation rules with unique design, (ii) hierarchical feature fusion network for predicting sub-features of terrain (iii) and a generator for outputting dense terrain categories of Mars. Finally, the results of actual data on Mars demonstrate that the terrain sub-features can be successfully recognized and a dense terrain classification map can be generated applying only coarse-grained labeled images.

关键词： Training Image recognition Annotations Biomimetics Pipelines Generators Robots

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Review of Inference Time Prediction Approaches of DNN: Emphasis on Service robots with cloud-edge-device architecture

Review of Inference Time Prediction Approaches of DNN: Empha...

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IEEE International Conference on robotics and Biomimetics

作者： Tian Xiang Qiwei Meng Ji Zhang Beibei Zhang Wei Song Anhuan Xie Jason Gu Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou China Department of Mechanical and Automation Engineering T Stone Robotics Institute The Chinese University of Hong Kong HKSAR China Research Center for Intelligent Computing Software Research Institute of Intelligent Computing Zhejiang Laboratory Hangzhou China Electrical and Computer Engineering Dalhousie University Halifax Canada

In recent years, the global robot market has witnessed substantial growth, particularly in the domain of service robots. Despite their expanding presence, service robots encounter limitations when operating autonomously in unstructured environments, primarily due to their constrained computational capacities. As a solution, the fusion of cloud and edge computing resources becomes imperative to expedite task inference and enhance scenario perception capabilities. The integration of cloud-edge-device models holds significant promise in bolstering the operational efficiency of robots. This entails the dynamic partitioning of intricate robotic tasks, executed collaboratively across cloud, edge, and device resources. In this landscape, deep neural network (DNN) models play a pivotal role in facilitating a wide array of robotic tasks. The inference time for each layer of a DNN model in actual deployment, emerges as a critical determinant in the model’s partitioning strategy. It also serves as an important metric influencing the model’s suitability for a specific hardware platform. This article presents an overview of recent advancements in predicting inference and training time of DNN models, summarizes the related methods, and finally discusses the challenges in this field and the research that can be studied in the future.

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HSMR: A Head-Shoulder Mask Aided ResNet to Guide Focus of Re-Identification Implemented on Tour-Guide Robot

HSMR: A Head-Shoulder Mask Aided ResNet to Guide Focus of Re...

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IEEE International Conference on robotics and Biomimetics

作者： Zheyuan Lin Min Huang Wen Wang Mengjie Qin Te Li Minhong Wan Jason Gu Shiqiang Zhu Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

ISBN: (纸本)9781665481106

For service robots, person re-identification (ReID) and multi-pedestrian tracking (MPT) are vital to get a person's location and link identities across frames. Though their accuracy keeps improving, most work lacks consideration of the application scenario, haunted by limited space, constrained power supply, and demands of the real-time response during human-robot interaction. Some ReID models learn trivial or unrelated features, inhibiting the downstream tasks. To solve these issues, the efficient and light-weighted Head-Shoulder Mask aided ResNet (HSMR) is proposed. This model applies multi-task learning to enhance the feature extraction performance in the training stage without extra computational load during inference. The auxiliary task fully uses head-shoulder information to guide the network and focuses on the head region, which contains the identity information. In experiments on the Tour-Guide Robot Data Base (TGRDB), HSMR earned results better than ResNet-18 on the ReID task and was superior to the recent two-stream method on the MPT task. On the mobile hardware, inference reaches an average of 15.2 FPS, three times faster than the two-stream method. The code is released at https://***/ZhYLin99/HSMR.

关键词： Training Service robots Computational modeling Robot vision systems Human-robot interaction Multitasking Real-time systems

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Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report 17th

Efficient Single-Image Depth Estimation on Mobile Devices, ...

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17th European Conference on computer vision, ECCV 2022

作者： Ignatov, Andrey Malivenko, Grigory Timofte, Radu Treszczotko, Lukasz Chang, Xin Ksiazek, Piotr Lopuszynski, Michal Pioro, Maciej Rudnicki, Rafal Smyl, Maciej Ma, Yujie Li, Zhenyu Chen, Zehui Xu, Jialei Liu, Xianming Jiang, Junjun Shi, XueChao Xu, Difan Li, Yanan Wang, Xiaotao Lei, Lei Zhang, Ziyu Wang, Yicheng Huang, Zilong Luo, Guozhong Yu, Gang Fu, Bin Li, Jiaqi Wang, Yiran Huang, Zihao Cao, Zhiguo Conde, Marcos V. Sapozhnikov, Denis Lee, Byeong Hyun Park, Dongwon Hong, Seongmin Lee, Joonhee Lee, Seunggyu Chun, Se Young Computer Vision Lab ETH Zürich Zürich Switzerland AI Witchlabs Zollikerberg Switzerland University of Wuerzburg Wuerzburg Germany TCL Research Europe Warsaw Poland Harbin Institute of Technology Harbin China Xiaomi Inc. Beijing China Tencent GY-Lab Shenzhen China National Key Laboratory of Science and Technology on Multi-Spectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of

ISBN: (纸本)9783031250651

Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks. Thus, it is very crucial to have efficient and accurate depth estimation models that can run fast on low-power mobile chipsets. In this Mobile AI challenge, the target was to develop deep learning-based single image depth estimation solutions that can show a real-time performance on IoT platforms and smartphones. For this, the participants used a large-scale RGB-to-depth dataset that was collected with the ZED stereo camera capable to generated depth maps for objects located at up to 50 m. The runtime of all models was evaluated on the Raspberry Pi 4 platform, where the developed solutions were able to generate VGA resolution depth maps at up to 27 FPS while achieving high fidelity results. All models developed in the challenge are also compatible with any Android or Linux-based mobile devices, their detailed description is provided in this paper. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image segmentation

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Modelling the Dependence of Chlorophyll Leaf-Clip Measures on Vegetation Indices Derived from Multispectral UAS Images in Vineyards Parcels

Modelling the Dependence of Chlorophyll Leaf-Clip Measures o...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Gil Gonçalves Umberto Andriolo Lúcio Paiva Cristiano Premebida António Ferraz Department of Mathematics University of Coimbra Coimbra Portugal Department of Electrical and Computer Engineering INESC Coimbra Coimbra Portugal Research Centre for Natural Resources Environment and Society Polytechnic Institute of Coimbra Coimbra Agrarian Technical School Coimbra Portugal Department of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Coimbra Portugal NASA Jet Propulsion Laboratory Pasadena United States

Multispectral images and leaf-clip measurements were used for evaluating the Chlorophyll (Chl) content on grapevine leaves through the use of vegetation indices. The multispectral leaf images were taken by a sensor onboard an UAS, which was placed over a table at a height of 70 cm. Images were radiometrically and geometrically processed to obtain accurate coregistered five band image stacks. Using a Kmeans segmentation, each leaf imaged in a multispectral image was automatically detected and the mean leaf reflectance values were used to compute three vegetation indices: NDVI, NDRE, and GLI. When compared to leaf-clip measurements, the results indicated that the NDRE index was the best predictor of Chl leaf content (R2=0.81). The obtained NDRE regression model can be used in UAS-based multispectral orthomosaics to generate canopy Chl maps at vine-row scale, which can assist vine growers in monitoring the spatial and temporal variability of grapevine vigor.

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Point Cloud Generation with Stereo and Monocular Depth Estimation on FPGA

Point Cloud Generation with Stereo and Monocular Depth Estim...

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2022 IEEE International Conference on Electro Information Technology, eIT 2022

作者： Perez-Vicente, Alejandro Hernandez, David Fang, Tianyang Saniie, Jafar Illinois Institute of Technology Embedded Computing and Signal Processing Department of Electrical and Computer Engineering Research Laboratory ChicagoIL60616 United States Universidad Politécnica de Madrid Etsi Industriales Madrid Spain

ISBN: (纸本)9781665480093

This research proposes a system that leverages stereo vision and monocular depth estimation to form a depth map from which a 3D point cloud scene is extracted. The emergence of competitive neural networks for depth map estimation presents an opportunity for deployment on embedded systems. Both stereo and monocular depth algorithms were tested on multiple FPGA hardware platforms. To perform acceleration on configurable hardware, a Deep Learning Processing Unit (DPU) IP was used for processing AI workloads, together with a Pseudo-LiDAR reprojection for 3D reconstruction on the Arm® processor. The DPU performance configuration will depend on the logic resources the targeted board has. This paper highlights the performance, resource utilization, and bottlenecks that came across different tested combinations of both hardware and neural network architectures. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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