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arXiv 2022年

作者： Poliakov, Vladimir Niu, Kenan Poorten, Emmanuel Vander Tsetserukou, Dzmitry The Intelligent Space Robotics Lab Skolkovo Institute of Science and Technology Moscow121205 Russia The Robot Assisted Surgery group Faculty of Mechanical Engineering KU Leuven Leuven3001 Belgium

This work presents an RL-based agent for outpatient hysteroscopy training. Hysteroscopy is a gynecological procedure for examination of the uterine cavity. Recent advancements enabled performing this type of intervention in the outpatient setup without anaesthesia. While being beneficial to the patient, this approach introduces new challenges for clinicians, who should take additional measures to maintain the level of patient comfort and prevent tissue damage. Our prior work has presented a platform for hysteroscopic training with the focus on the passage of the cervical canal. With this work, we aim to extend the functionality of the platform by designing a subsystem that autonomously performs the task of the passage of the cervical canal. This feature can later be used as a virtual instructor to provide educational cues for trainees and assess their performance. The developed algorithm is based on the soft actor critic approach to smooth the learning curve of the agent and ensure uniform exploration of the workspace. The designed algorithm was tested against the performance of five clinicians. Overall, the algorithm demonstrated high efficiency and reliability, succeeding in 98% of trials and outperforming the expert group in three out of four measured metrics. © 2022, CC BY.

关键词： Endoscopy

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PointStack: A Point Cloud Processing Network with Enhanced Global Feature Aggregation

PointStack: A Point Cloud Processing Network with Enhanced G...

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Software engineering and Computer science (CSECS), International Conference on

作者： Yongliang Tao Jintao Wang Pengchao Li Mingmin Liu Zhenjun Du Yang Liu Chengpeng Li Faculty of Robot Science and Engineering Northeastern university Shenyang China Central Research Academy ShanDong SIASUN Industrial Software Research Institute Co. Ltd Shandong China Central Research Academy SIASUN Robot & Automation Co. Ltd Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

Point cloud data, characterized by its rich geometric information, dense data representation, and flexible topological structure, finds extensive application in fields such as computer graphics, computer vision, and robotics perception. The rapid advancement and upgrade of three-dimensional point cloud acquisition devices have made the acquisition of point cloud data increasingly convenient. This, in turn, has spurred an urgent need for research on point cloud data processing. The irregularity and disorderliness of point clouds necessitate the design of network models that differ from those used for image processing. Presently, most network models for point cloud processing focus primarily on the design for local feature extraction, even though the effectiveness of these improvements has become somewhat marginal. In response to this, a stacked diffusion network model designed to capture global context has been proposed. This model excels at capturing global information and can effectively integrate it with local information. Extensive experiments conducted on standard benchmark datasets such as ModelNet40 and ShapeNetPart indicate that this model, in comparison to DGCNN, improves point cloud classification by 1.2%, achieving an Overall Accuracy (OA) of 94.1%. Furthermore, it enhances point cloud part segmentation by 1.3%, reaching an mIOU evaluation metric of 86.5%. When compared to other advanced methods, this model also exhibits noticeable improvements in point cloud classification and part segmentation.

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Face Liveness Detection Algorithm based on Livenesslight Network

Face Liveness Detection Algorithm based on Livenesslight Net...

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2020 International Conference on High Performance Big Data and Intelligent Systems, HPBD and IS 2020

作者： Zuo, Yinlong Gao, Wenlong Wang, Jintao Faculty of Robot Science and Engineering Northeastern University Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9781728165110

Face liveness detection is the first stage of the whole face detection technology, and it is of great significance to the system security using face recognition technology. In this paper we use convolutional neural networks to extract facial features. Compared to ResNet, SqueezeNet and VGG Face networks, the network structure is lighter and the model training takes less time. After testing, it achieved an accuracy of 99.5% on the Nanjing University of Aeronautics and Astronautics (NUAA) face detection data set of China Southern Airlines. In order to improve the robustness of the model, we built our own dataset through collecting pictures from internet and camera shooting, and horizontally compared the accuracy of the above several models on the self-built dataset. Finally the model proposed in this paper achieved the highest 99.02% accuracy and achieved the best results in actual testing. © 2020 IEEE.

关键词： Face recognition

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Improved signed distance function for 2D real-time SLAM and accurate localization

arXiv

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arXiv 2021年

作者： Fu, Xingyin Fang, Zheng Xiao, Xizhen He, Yijia Liu, Xiao Technology Inc. Beijing China Faculty of Robot Science and Engineering Northeastern University Shenyang110169 China

Accurate mapping and localization are very important for many industrial robotics applications. In this paper, we propose an improved Signed Distance Function (SDF) for both 2D SLAM and pure localization to improve the accuracy of mapping and localization. To achieve this goal, firstly we improved the back-end mapping to build a more accurate SDF map by extending the update range and building free space, etc. Secondly, to get more accurate pose estimation for the front-end, we proposed a new iterative registration method to align the current scan to the SDF submap by removing random outliers of laser scanners. Thirdly, we merged all the SDF submaps to produce an integrated SDF map for highly accurate pure localization. Experimental results show that based on the merged SDF map, a localization accuracy of a few millimeters (5mm) can be achieved globally within the map. We believe that this method is important for mobile robots working in scenarios where high localization accuracy matters. © 2021, CC BY.

关键词： Mapping

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Super odometry: IMU-centric LiDAR-visual-inertial estimator for challenging environments

arXiv

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arXiv 2021年

作者： Zhao, Shibo Zhang, Hengrui Wang, Peng Nogueira, Lucas Scherer, Sebastian Robotics Institute Carnegie Mellon University United States Faculty of Robot Science and Engineering Northeastern University China

We propose Super Odometry, a high-precision multi-modal sensor fusion framework, providing a simple but effective way to fuse multiple sensors such as LiDAR, camera, and IMU sensors and achieve robust state estimation in perceptually-degraded environments. Different from traditional sensor-fusion methods, Super Odometry employs an IMU-centric data processing pipeline, which combines the advantages of loosely coupled methods with tightly coupled methods and recovers motion in a coarse-to-fine manner. The proposed framework is composed of three parts: IMU odometry, Visual-inertial odometry, and LiDAR-inertial odometry. The Visual-inertial odometry and LiDAR-inertial odometry provide the pose prior to constrain the IMU bias and receive the motion prediction from IMU odometry. To ensure high performance in real-time, we apply a dynamic octree that only consumes 10% of the running time compared with a static KD-tree. The proposed system was deployed on drones and ground robots, as part of Team Explorer's effort to the DARPA Subterranean Challenge where the team won 1st and 2nd place in the Tunnel and Urban Circuits 1, respectively. © 2021, CC BY-NC-SA.

关键词： Motion estimation

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Reducing-Over-Time Tree for Event-based Data

Reducing-Over-Time Tree for Event-based Data

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International Conference on Pattern Recognition

作者： Shane Harrigan Sonya Coleman Dermot Kerr Pratheepan Yogarajah Zheng Fang Chengdong Wu School of Computing Engineering and Intelligent Systems Ulster University Northern Ireland UK Faculty of Robot Science and Engineering Northeastern University Liaoning China

This paper presents a novel Reducing-Over-Time (ROT) binary tree structure for event-based vision data and subtypes of the tree structure. A framework is presented using ROT, that takes advantage of the self-balancing and self-pruning nature of the tree structure to extract spatial-temporal information. The ROT framework is paired with an established motion classification technique and performance is evaluated against other state-of-the-art techniques using four datasets. Additionally, the ROT framework as a processing platform is compared with other event-based vision processing platforms in terms of memory usage and is found to be one of the most memory efficient platforms available.

关键词： Matched filters Image edge detection Memory management Binary trees Data mining Corner detection

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Event-Based Stereo Depth Estimation With Motion Guidance and Left–Right Consistency

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Jiang, Junjie Zhuang, Hao Huang, Xinjie Kong, Delei Fang, Zheng Northeastern University Faculty of Robot Science and Engineering Shenyang110819 China Hunan University School of Robotics Changsha410082 China

Depth estimation, which directly captures the structure of observable environmental surfaces, plays a critical role in vision-based applications such as measurement, mapping, autonomous driving, and robot navigation. Specifically, event camera-based stereo depth estimation provides a novel solution to challenging conditions like rapid motion and extreme illumination variations. Currently, deep learning has become the dominant approach for event camera-based stereo depth estimation. However, these methods fail to fully exploit temporal cues in the event stream, resulting in suboptimal event representation clarity. Furthermore, there is room for further reduction in pixel shifts in feature maps before constructing the cost volume. This article presents a novel event-based stereo depth estimation method with motion guidance and left-right consistency. First, an edge-aware aggregation (EAA) module is proposed, which integrates event frames with motion confidence maps to generate a novel high-definition event representation. Then, a motion-guided attention (MGA) module is introduced, which leverages deformable transformer encoders guided by motion confidence maps to refine edge precision in feature maps. Finally, a census left-right consistency loss function is designed to enhance the left-right consistency of stereo event representation. Experiments conducted in challenging real-world driving scenarios demonstrate that the proposed method outperforms state-of-the-art (SOTA) methods in terms of mean absolute error (MAE) and root mean square error (RMSE) metrics. © 1963-2012 IEEE.

关键词： Stereo image processing

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Heart Position Estimation based on Bone Distribution toward Autonomous robotic Fetal Ultrasonography

Heart Position Estimation based on Bone Distribution toward ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Yuuki Shida Ryosuke Tsumura Takabumi Watanabe Hiroyasu Iwata Graduate School of Creative Science and Engineering Waseda University Tokyo Japan Global Robot Academia Laboratory Waseda University Faculty of Science and Engineering Waseda University Tokyo Japan

Autonomous fetal ultrasonography with a robotic ultrasound (US) can potentially solve the issue of the shortage of ob-gyn physicians in prenatal care. In fetal cardiac diagnosis, acoustic shading derived from the fetal skeleton makes the scanning procedure using a robotic US cumbersome. We hypothesize that fetal bone distribution can be used for determining the fetal position and for subsequently estimating the heart position. This paper serves a method for estimating fetal heart position for autonomous cardiac diagnosis. Our proposed system is comprised of following three processes. First, the fetal bone distribution is generated by following steps: detect shadow areas derived from fetal bones; calculate the three-dimensional (3D) position of the bones; and identify bone types (skull, spine, and others) based on its shadow features. Next, the fetal head position and body axis are estimated based on the generated 3D bone distribution. Finally, the fetal chest position and thoracic sagittal axis of the fetus are estimated based on anatomical structures, and the position of the heart is determined. We validated the proposed method by evaluating the accuracies of bone detection and heart position estimation with three pregnant volunteers. The false positive rate of bone detection is less than 10%, which is sufficient for the proposed method; further, the estimation error for the heart position was within an acceptable error of 15 mm. These results demonstrate the sufficient accuracy of the proposed estimation method and suggest the potential of autonomous fetal cardiac diagnosis using robotic US scanning.

关键词： Pregnancy Estimation error Three-dimensional displays Ultrasonography Anatomical structure Bones Fetus

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Research on Obstacle Avoidance Planning of Serpentine Manipulator Based on Particle Swarm Optimization

Research on Obstacle Avoidance Planning of Serpentine Manipu...

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Chinese Automation Congress (CAC)

作者： Haoxiang Su Manlu Liu Liguo Tan Jianwen Huo Qing Su Yang Jiang School of Information Engineering Southwest University of Science and Technology Mianyang China Robot Technology Used for Special Environment Key Laboratory of Sichuan Province Southwest University of Science and Technology Mianyang China School of Information Engineering Robot Technology Used for Special Environment Key Laboratory of Sichuan Province Southwest University of Science and Technology Mianyang China Tianfu Institute of Research and Innovation Southwest University of Science and Technology Chengdu China School of Harbin Institute of Technology Research Center of Basic Space Science Harbin China Manufacturing Science and Engineering Faculty Southwest University of Science and Technology Mianyang China

Compared with traditional manipulators, serpentine manipulator have the advantage of high flexibility and have broad prospects in exploring confined spaces. Aiming at the problems of large amount of calculation and poor obstacle avoidance effect in motion planning of serpentine arm in complex space environment. In this paper, combined with the particle swarm algorithm, a "head traction" path tracking method is designed, so that the rope-driven serpentine manipulator can efficiently move along the planned path in complex terrain. In the method of this paper, the obstacle modeling is completed first, and then a serpentine arm path planning algorithm based on particle swarm optimization is further designed. Use the planned path as the path point tracked by the serpentine manipulator "head". As the manipulator moves forward, all joints follow the path traveled by the manipulator's "head". In the final stage, in order to ensure that the manipulator can accurately reach the target point, an end position adjustment algorithm is designed. Through the simulation experiment of the rope-driven serpentine manipulator based on 21 degrees of freedom, it is proved that the method can successfully make the serpentine manipulator move in complex space, and has high computational efficiency and high precision, which is well verified the validity of the study.

关键词： Target tracking Heuristic algorithms Prototypes Arms Manipulators Path planning Planning

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Two-Steam Fully Connected Graph Convolutional Network for Skeleton-Based Action Recognition

Two-Steam Fully Connected Graph Convolutional Network for Sk...

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第32届中国控制与决策会议

作者： Zhongyu Bai Qichuan Ding Jiawei Tan Faculty of Robot Science and Engineering Northeastern University

Skeleton-based human action recognition has recently drawn a lot of attentions with the increasing availability of large-scale skeleton *** Convolutional Network(GCN) methods have achieved relatively good performances in action ***,most GCN methods based on predefined graphs with fixed topology constraints always neglect the potential dependencies derived from the cooperative movement of all ***,the lengths and the directions of skeletons are rarely *** easily cause a larger deviation of the estimated action from the actual ***,a two-steam fully connected graph convolutional network(2 s-FGCN) is *** topology structure of the 2 s-FGCN covers the local physical connections and the global potential cooperation of all joints and the joints,lengths and directions of skeletons are all input to the *** experimental results on two datasets(NTU-RGB+D and Kinetics-Skeleton) demonstrate that the proposed model can obtain the state-of-the-art results.

关键词： Action recognition Graph convolutional network(GCN) Skeleton datasets Two-stream framework

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