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LDFCDet: Boosting 3D Object Detectors with Low-High level Feature Crosses Using Laplace Distribution

IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： He, Zhenyu Mao, Jianxu Wang, Yaonan Yu, Junlong Tao, Ziming Yi, Junfei Zhang, Hui Wang, Shaoyuan Hunan University College of Electrical and Information Engineering Hunan Changsha410012 China Hunan University School of Robotics Changsha410082 China National Engineering Reseach Center for Robot Visual Perception and Control Technology Changsha410082 China

Highly accurate 3D object detection is critical for autonomous driving and robotic sensing system. However, some objects with few foreground points significantly affect the accuracy of 3D object detection. As the network depth increases, the low-level features of these objects are gradually lost, especially for the hard object. Due to this issue, current LiDAR-only based and multimodal methods often misclassify background as foreground. Therefore, how to leverage the low-level feature that contain information about these objects in the high layer of the network becomes the key to optimizing the issue. In this paper, we propose LDFCDet, a framework boosting 3D object detectors with low-high level feature crosses using Laplace distribution(LD). In our proposed method, we design a low-high level feature crosses module(LHFCM) to embed low-level feature into high-level feature in the deeper layer of the network, and use Laplace distribution to obtain a new low-high level feature that includes information about these objects with few foreground points. In addition, we propose a res-gated feature aggregation module(RGFAM) to fuse the mutli-scale features. Our approach is well-suited for both LiDAR-based and multimodal *** evaluate the LDFCDet on the widely used KITTI dataset, and our method outperforms almost current 3D object detection methods on the challenging KITTI test set. Moreover, we conducted comparative experiments on the ONCE dataset, and the results further demonstrate the effectiveness and superiority of our method. © 1991-2012 IEEE.

关键词： Object detection

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Voxel or Pillar: Exploring Efficient Point Cloud Representation for 3D Object Detection

arXiv

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arXiv 2023年

作者： Huang, Yuhao Zhou, Sanping Zhang, Junjie Dong, Jinpeng Zheng, Nanning National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University China

Efficient representation of point clouds is fundamental for LiDAR-based 3D object detection. While recent grid-based detectors often encode point clouds into either voxels or pillars, the distinctions between these approaches remain under-explored. In this paper, we quantify the differences between the current encoding paradigms and highlight the limited vertical learning within. To tackle these limitations, we introduce a hybrid Voxel-Pillar Fusion network (VPF), which synergistically combines the unique strengths of both voxels and pillars. Specifically, we first develop a sparse voxel-pillar encoder that encodes point clouds into voxel and pillar features through 3D and 2D sparse convolutions respectively, and then introduce the Sparse Fusion Layer (SFL), facilitating bidirectional interaction between sparse voxel and pillar features. Our efficient, fully sparse method can be seamlessly integrated into both dense and sparse detectors. Leveraging this powerful yet straightforward framework, VPF delivers competitive performance, achieving real-time inference speeds on the nuScenes and Waymo Open Dataset. The code will be available at https://***/HuangYuhao-0623/VPF. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

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Fuzzy Sliding Mode Control of Flying Manipulator Based on Exponential Reaching Law 6

Fuzzy Sliding Mode Control of Flying Manipulator Based on Ex...

引用

6th International Conference on Control, robotics and Cybernetics, CRC 2021

作者： Liu, Yunfei Wang, Weijun Guo, Qiang The 21ST Research Institute of China Electronic Technology Group Corporation Robotics Engineering Center China

ISBN: (纸本)9781665424370

In order to achieve the purpose of keeping the trajectory of the end of the manipulator stable when the flying manipulator is subjected to internal disturbances and external disturbances, this paper designs a sliding mode control algorithm based on the exponential reaching law to strengthen the anti-interference However, this method will cause chattering of the control law due to the larger upper bound of the interference. Therefore, a fuzzy sliding mode control method is designed on the basis of the above algorithm, and the coefficient of the switching gain term in the exponential reaching law sliding mode control algorithm is adaptively adjusted through fuzzy control to weaken the chattering of the control law. And use the Lyapunov stability criterion to prove the stability of the control system. And through simulation, the method not only retains the robustness of the exponential reaching law sliding mode control algorithm, but also effectively weakens the chattering problem of the sliding mode control law, and improves the estimated tracking control of the end of the flying manipulator. © 2021 IEEE.

关键词： Sliding mode control

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Probabilistic Visibility-Aware Trajectory Planning for Target Tracking in Cluttered Environments

Probabilistic Visibility-Aware Trajectory Planning for Targe...

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American Control Conference (ACC)

作者： Han Gao W. Pengying S. Yao Kangjie Zhou M. Ji Hangxin Liu Chang Liu Department of Advanced Manufacturing and Robotics College of Engineering Peking University National Key Laboratory of General Artificial Intelligence Beijing Institute for General Artificial Intelligence (BIGAI)

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

Target tracking has numerous significant civilian and military applications, and maintaining the visibility of the target plays a vital role in ensuring the success of the tracking task. Existing visibility-aware planners primarily focus on keeping the target within the limited field of view of an onboard sensor and avoiding obstacle occlusion. However, the negative impact of system uncertainty is often neglected, rendering the planners delicate to uncertainties in practice. To bridge the gap, this work proposes a model predictive control (MPC)-based trajectory planner for real-time visibility-aware and safe target tracking in the presence of system uncertainty. For more accurate target motion prediction, we introduce the concept of belief-space probability of detection (BPOD) to measure the predictive visibility of the target under stochastic robot and target states. An Extended Kalman Filter variant incorporating BPOD is developed to predict target belief state under uncertain visibility within the planning horizon. To reach real-time trajectory planning, we propose a computationally efficient algorithm to uniformly calculate both BPOD and the chance-constrained collision risk by utilizing linearized signed distance function (SDF), and subsequently solve the MPC problem by sequential convex programming. Extensive simulation results with benchmark comparisons show the capacity of the proposed approach to robustly maintain the visibility of the target under high system uncertainty. The practicality of the proposed trajectory planner is validated by real-world experiments.

关键词： Uncertainty Target tracking Trajectory planning Stochastic systems Simulation Stochastic processes Robot sensing systems

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Online Robot Motion Planning Methodology Guided by Group Social Proxemics Feature

arXiv

引用

arXiv 2025年

作者： Mu, Xuan Liu, Xiaorui Guo, Shuai Chi, Wenzheng Wang, Wei Ge, Shuzhi Sam School of Automation Qingdao University Qingdao266000 China School of Automation Qingdao University Qingdao University Qingdao266000 China Robotics and Microsystems Center School of Mechanical and Electric Engineering Soochow University Suzhou215021 China School of Mechanical Engineering & Automation the Robotics Institute Beihang University Beijing100000 China Department of Electrical and Computer Engineering National University of Singapore Singapore117576 Singapore

Nowadays robot is supposed to demonstrate human-like perception, reasoning and behavior pattern in social or service application. However, most of the existing motion planning methods are incompatible with above requirement. A potential reason is that the existing navigation algorithms usually intend to treat people as another kind of obstacle, and hardly take the social principle or awareness into consideration. In this paper, we attempt to model the proxemics of group and blend it into the scenario perception and navigation of robot. For this purpose, a group clustering method considering both social relevance and spatial confidence is introduced. It can enable robot to identify individuals and divide them into groups. Next, we propose defining the individual proxemics within magnetic dipole model, and further established the group proxemics and scenario map through vector-field superposition. On the basis of the group clustering and proxemics modeling, we present the method to obtain the optimal observation positions (OOPs) of group. Once the OOPs grid and scenario map are established, a heuristic path is employed to generate path that guide robot cruising among the groups for interactive purpose. A series of experiments are conducted to validate the proposed methodology on the practical robot, the results have demonstrated that our methodology has achieved promising performance on group recognition accuracy and path-generation efficiency. This concludes that the group awareness evolved as an important module to make robot socially behave in the practical scenario. © 2025, CC BY-NC-ND.

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Cyberbullying Classification Using a Natural Language Processing and Machine Learning Techniques

Cyberbullying Classification Using a Natural Language Proces...

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2024 International Conference on Advances in Computing Research on Science engineering and Technology, ACROSET 2024

作者： Mankar, Nikhilesh Pramod Mahure, Tushar Joshi, Anushka A. Pande, Sagar Dhanraj Khamparia, Aditya Nathe, Ankita Shrikrushna Turjman, Fadi Al Pune India Independent Researcher Amravati India Babasaheb Bhimrao Ambedkar University Department of Computer Science Amethi India Kavikulguru Institute of Technology and Science Department of Information Technology Nagpur India Ai and Robotics Institute Near East University Artificial Intelligence Software Information Systems Engineering Departments Nicosia Cyprus University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Turkey

ISBN: (纸本)9798350388800

The usage of online entertainment has increased dramatically after some time with the enhancement of the Internet and has turned into the most compelling systems administration stage in this century. Notwithstanding, the upgrade of a social network regularly makes adverse consequences on society that add to two or three terrible peculiarities, for example, online maltreatment, badgering cyberbullying, cybercrime, and internet savaging. Cyberbullying habitually prompts genuine mental and actual misery, especially for ladies and kids, and even occasionally make them to use self-destruction methods. Multiple number of episodes have as of late happened worldwide because of online badgering, like sharing private talks, bits of hearsay, and sexual comments. In this way, the distinguishing proof of harassing messages or messages via online entertainment has gained a significant measure for development for consideration with in scientists. The ground or motive for this exploration is to create a full-fledged way or a method to differentiate online harmful and tormenting drafts by combining various methods and techniques in the domain of natural language processing and AI © 2024 IEEE.

关键词： Adversarial machine learning

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Contralateral Movement Constraint Based Gait Intervention Using a Powered Hip Exoskeleton 8

Contralateral Movement Constraint Based Gait Intervention Us...

引用

8th IEEE International Conference on Advanced robotics and Mechatronics, ICARM 2023

作者： Wang, Zilu Zhou, Zhihao Wang, Qining College of Engineering Peking University Beijing100871 China Institute for Artificial Intelligence Peking University Beijing100871 China Beijing Engineering Research Center of Intelligent Rehabilitation Engineering Beijing100871 China Department of Advanced Manufacturing and Robotics College of Engineering The Institute for Artificial Intelligence Peking University Beijing100871 China University of Health and Rehabilitation Sciences Qingdao266071 China National Key Laboratory of General Artificial Intelligence BIGAI Beijing100080 China

ISBN: (纸本)9798350300178

Hemiparetic gait patterns of stroke survivors has been widely focused on nowadays with various robot-assist gait intervention approaches. In this paper, we use a powered hip exoskeleton to provide resistance on one side of the wearer during walking and explored the variation of the other side of the lower limb in kinematics, ground reaction forces, and EMG signals. The preliminary results on healthy adults indicated that the difference in hip joint angles, EMG area under the curve, co-contraction index, and synergy weight were observed under the contra-lateral intervention. This study shows potentials that powered hip exoskeleton-induced non-hemiplegic side constraints may be a new rehabilitation way for stroke patients in the near future. © 2023 IEEE.

关键词： Patient rehabilitation

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Analysis of the Perpendicularity Influence of the Gyrowheel Support on the Spacecraft Angular Velocity Measurement Accuracy

Analysis of the Perpendicularity Influence of the Gyrowheel ...

引用

International Conference on Guidance, Navigation and Control, ICGNC 2020

作者： Xiaokun, Liu Jian, Zhang Jun, Fu Dongqing, Gu Bin, Song Aerospace System Engineering Shanghai Shanghai China Shanghai Engineering Research Center of Space Robotics Shanghai China Shanghai Academy of Spaceflight Technology Shanghai China Beijing Institute of Tracking and Telecommunication Technology Beijing China

ISBN: (纸本)9789811581540

The gyrowheel can be used as an actuator to provide three-axis control torque for spacecraft, and also as a sensor to measure the two-axis attitude angular velocity of spacecraft. The existence of non ideal factors is the key factor affecting the measurement of spacecraft angular velocity with gyrowheel. Therefore, this paper mainly studies the non-ideal factor that the support torsion bar of gyrowheel is not perpendicular and analyzes the non-perpendicularity support on the measurement accuracy of spacecraft attitude angular velocity. © 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Spacecraft

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ESVO2: Direct Visual-Inertial Odometry with Stereo Event Cameras

arXiv

引用

arXiv 2024年

作者： Niu, Junkai Zhong, Sheng Lu, Xiuyuan Shen, Shaojie Gallego, Guillermo Zhou, Yi School of Robotics Hunan University Changsha China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong TU Berlin The Science of Intelligence Excellence Cluster The Robotics Institute Germany Einstein Center Digital Future Berlin Germany

Event-based visual odometry is a specific branch of visual Simultaneous Localization and Mapping (SLAM) techniques, which aims at solving tracking and mapping sub-problems (typically in parallel), by exploiting the special working principles of neuromorphic (i.e., event-based) cameras. Due to the motion-dependent nature of event data, explicit data association (i.e., feature matching) under large-baseline viewpoint changes is difficult to establish, making direct methods a more rational choice. However, state-of-the-art direct methods are limited by the high computational complexity of the mapping sub-problem and the degeneracy of camera pose tracking in certain degrees of freedom (DoF) in rotation. In this paper, we tackle these issues by building an event-based stereo visual-inertial odometry system on top of a direct pipeline [1]. Specifically, to speed up the mapping operation, we propose an efficient strategy for sampling contour points according to the local dynamics of events. The mapping performance is also improved in terms of structure completeness and local smoothness by merging the temporal stereo and static stereo results. To circumvent the degeneracy of camera pose tracking in recovering the pitch and yaw components of general 6-DoF motion, we introduce IMU measurements as motion priors via pre-integration. To this end, a compact back-end is proposed for continuously updating the IMU bias and predicting the linear velocity, enabling an accurate motion prediction for camera pose tracking. The resulting system scales well with modern high-resolution event cameras and leads to better global positioning accuracy in large-scale outdoor environments. Extensive evaluations on five publicly available datasets featuring different resolutions and scenarios justify the superior performance of the proposed system against five state-of-the-art methods. Compared to ESVO [1], our new pipeline significantly reduces the camera pose tracking error by 40%–80% and 20%—80%

关键词： Open source software

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ASPIRe: An Informative Trajectory Planner with Mutual Information Approximation for Target Search and Tracking

ASPIRe: An Informative Trajectory Planner with Mutual Inform...

引用

IEEE International Conference on robotics and Automation (ICRA)

作者： Kangjie Zhou Pengying Wu Yao Su Han Gao Ji Ma Hangxin Liu Chang Liu Department of Advanced Manufacturing and Robotics College of Engineering Peking University National Key Laboratory of General Artificial Intelligence Beijing Institute for General Artificial Intelligence (BIGAI)

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper proposes an informative trajectory planning approach, namely, adaptive particle filter tree with sigma point-based mutual information reward approximation (ASPIRe), for mobile target search and tracking (SAT) in cluttered environments with limited sensing field of view. We develop a novel sigma point-based approximation to accurately estimate mutual information (MI) for general, non-Gaussian distributions utilizing particle representation of the belief state, while simultaneously maintaining high computational efficiency. Building upon the MI approximation, we develop the Adaptive Particle Filter Tree (APFT) approach with MI as the reward, which features belief state tree nodes for informative trajectory planning in continuous state and measurement spaces. An adaptive criterion is proposed in APFT to adjust the planning horizon based on the expected information gain. Simulations and physical experiments demonstrate that ASPIRe achieves real-time computation and outperforms benchmark methods in terms of both search efficiency and estimation accuracy.

关键词： Accuracy Target tracking Trajectory planning Robot sensing systems Real-time systems Particle filters Trajectory

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