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Yonghao Pang - IEEE Xplore Author Profile

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Author details

Yonghao Pang

Publications

5

Citations

171

Publications by Year

20202025

Co-Authors:

Yumei CaiQian GuoXu GuoPeng JiangShucai Li

Show All Co-Authors (17)

Yonghao Pang

Affiliation

Zhejiang Huadong Geotechnical Investigation and Design Institute Co

Ltd

Hangzhou, China

Publication Topics

Convolutional Neural Network,
Inversion Results,
Resistance Model,
Apparent Resistivity,
Forward Model,
Inverse Method,
Inverse Problem,
Neural Network,
Realistic Model,
Survey Line,
Adaptive Convolution,
Adaptive Kernel

Biography

Yonghao Pang is currently pursuing the Ph.D. degree with Shandong University, Jinan, China, in 2019. He is mainly engaged in the research work of geophysical inversion theory.(Based on document published on 16 September 2020).

Publications

5

Citations

171

Publications by Year

20202025

Co-Authors:

Yumei Cai
Qian Guo
Xu Guo
Peng Jiang
Shucai Li

Show All Co-Authors (17)

Author's Published Works

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Showing 1-25 of 46 results

Conferences (27)

Journals (17)

Early Access Articles (2)

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Yisha Liu(44)
Yan Zhuang(31)
Xuetao Zhang(18)
Xuebo Zhang(14)
Hanzhang Wang(11)
School of Information Science and Technology, Dalian Maritime University, Dalian, China(17)
School of Control Science and Engineering, Dalian University of Technology, Dalian, China(10)
Information Science and Technology College, Dalian Maritime University, Dalian, China(8)
Research Center of Information and Control, Dalian University of Technology, Dalian, China(6)
Intelligent Robotic Laboratory, School of Control Science and Engineering, Dalian University of Technology, Dalian, China(5)
IEEE Transactions on Industrial Electronics(3)
IEEE Transactions on Instrumentation and Measurement(3)
2012 International Conference on System Science and Engineering (ICSSE)(2)
2023 13th International Conference on Information Science and Technology (ICIST)(2)
2023 42nd Chinese Control Conference (CCC)(2)
IEEE(46)
Media(10)
Datasets(1)
Dalian, China(4)
Cairo, Egypt(2)
Tianjin, China(2)
Abu Dhabi, United Arab Emirates(1)
Boston, MA, USA(1)
Unmanned Aerial Vehicles(17)
Point Cloud(13)
Path Planning(10)
Inertial Measurement Unit(7)
Obstacle Avoidance(7)

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Results

Enhanced Infrared-Visible Image Fusion Method with an ISP Based Degradation Model

Xueyi Guo;Yisha Liu;Weimin Xue;Zhiwei Zhang

2025 13th International Conference on Intelligent Control and Information Processing (ICICIP)

Year: 2025 | Conference Paper |

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The performance of infrared (IR) and visible (VIS) image fusion tasks depends on the quality of source images, which are usually impacted by various degradation factors in real-world scenarios, leading to poor quality and visual effects of the fused images. By learning the mapping from low-quality (LQ) source images to high-quality (HQ) fused images, neural networks can remove the impacts of degra...Show More

Enhanced Infrared-Visible Image Fusion Method with an ISP Based Degradation Model

Xueyi Guo;Yisha Liu;Weimin Xue;Zhiwei Zhang

2025 13th International Conference on Intelligent Control and Information Processing (ICICIP)

Year: 2025 | Conference Paper |

Low-Light Enhancement and Global-Local Feature Interaction for RGB-T Semantic Segmentation

Xueyi Guo;Yisha Liu;Weimin Xue;Zhiwei Zhang;Yan Zhuang

IEEE Transactions on Instrumentation and Measurement

Year: 2025 | Volume: 74 | Journal Article |

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The performance of RGB-T semantic segmentation tasks is affected by the quality of visible (VIS) and infrared (IR) images captured by sensor instruments. In low-light environments, various degradation factors lead to the poor quality of captured VIS and IR images, ultimately reducing the performance of subsequent semantic segmentation tasks. To address this issue, we propose a novel RGB-T semantic...Show More

Low-Light Enhancement and Global-Local Feature Interaction for RGB-T Semantic Segmentation

Xueyi Guo;Yisha Liu;Weimin Xue;Zhiwei Zhang;Yan Zhuang

IEEE Transactions on Instrumentation and Measurement

Year: 2025 | Volume: 74 | Journal Article |

CLID-SLAM: A Coupled LiDAR-Inertial Neural Implicit Dense SLAM With Region-Specific SDF Estimation

Junlong Jiang;Xuetao Zhang;Gang Sun;Yisha Liu;Xuebo Zhang;Yan Zhuang

IEEE Robotics and Automation Letters

Year: 2025 | Volume: 10, Issue: 4 | Journal Article |

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This letter proposes a novel scan-to-neural model matching, tightly-coupled LiDAR-inertial Simultaneous Localization and Mapping (SLAM) system, which can achieve more accurate state estimation and incrementally reconstruct the dense map. Different from the existing methods, the key insight of the proposed approach is that region-specific Signed Distance Function (SDF) estimations supervise the neu...Show More

CLID-SLAM: A Coupled LiDAR-Inertial Neural Implicit Dense SLAM With Region-Specific SDF Estimation

Junlong Jiang;Xuetao Zhang;Gang Sun;Yisha Liu;Xuebo Zhang;Yan Zhuang

IEEE Robotics and Automation Letters

Year: 2025 | Volume: 10, Issue: 4 | Journal Article |

PCDCT: Perception-Complementarity-Driven Collaborative Trajectory Generation for Vision-Based Aerial Tracking

Hanzhang Wang;Xuetao Zhang;Yisha Liu;Gang Sun;Xuebo Zhang;Yan Zhuang

IEEE Transactions on Automation Science and Engineering

Year: 2025 | Early Access Article |

This article proposes a perception-complementarity-driven trajectory generating method for multiple unmanned aerial vehicles (UAVs), which can effectively enhance the visibility of the target for UAVs in unknown environments. rgb0,0,0Traditional methods often rely on prior maps or additional sensors to assist with obstacle avoidance. Nevertheless, these methods are not only costly but also fail to...Show More

PCDCT: Perception-Complementarity-Driven Collaborative Trajectory Generation for Vision-Based Aerial Tracking

Hanzhang Wang;Xuetao Zhang;Yisha Liu;Gang Sun;Xuebo Zhang;Yan Zhuang

IEEE Transactions on Automation Science and Engineering

Year: 2025 | Early Access Article |

Semantic-Driven Informed Planning and 3D Reconstruction for the Quadrotor Unmanned Aerial Vehicle

Xiaotian Xu;Xuetao Zhang;Yisha Liu;Hanzhang Wang;Xuebo Zhang;Yan Zhuang

IEEE Transactions on Vehicular Technology

Year: 2025 | Volume: 74, Issue: 3 | Journal Article |

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Autonomous aerial reconstruction tackles the problem of deploying the quadrotor unmanned aerial vehicle in an unknown environment for vision-based surface reconstruction. The state-of-the-art methods mainly extend the classic next-best-view (NBV) framework with the semantic information to concentrate on the reconstruction target. However, the greedy decision of the next viewpoint or branch without...Show More

Semantic-Driven Informed Planning and 3D Reconstruction for the Quadrotor Unmanned Aerial Vehicle

Xiaotian Xu;Xuetao Zhang;Yisha Liu;Hanzhang Wang;Xuebo Zhang;Yan Zhuang

IEEE Transactions on Vehicular Technology

Year: 2025 | Volume: 74, Issue: 3 | Journal Article |

A Multi-UAV Monitoring and Search Strategy Based on Multi-Agent Reinforcement Learning

Shuyi Guo;Xuetao Zhang;Hanzhang Wang;Gang Sun;Yisha Liu;Yan Zhuang

2024 IEEE International Conference on Unmanned Systems (ICUS)

Year: 2024 | Conference Paper |

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Multiple UAVs have been widely used for targets search and monitoring. Nonetheless, in practice, this problem is challenging as targets usually move randomly, but the trajectories of the targets cannot be predicted in advance by UAV swarms. Moreover, the efficiency of exploration, which indirectly impacts monitoring, is low when the number of targets in the environment is unknown. A Search Extende...Show More

A Multi-UAV Monitoring and Search Strategy Based on Multi-Agent Reinforcement Learning

Shuyi Guo;Xuetao Zhang;Hanzhang Wang;Gang Sun;Yisha Liu;Yan Zhuang

2024 IEEE International Conference on Unmanned Systems (ICUS)

Year: 2024 | Conference Paper |

MUP-LIO: Mapping Uncertainty-aware Point-wise Lidar Inertial Odometry

Hekai Yao;Xuetao Zhang;Gang Sun;Yisha Liu;Xuebo Zhang;Yan Zhuang

2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Year: 2024 | Conference Paper |

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This paper proposes a mapping uncertainty-aware point-wise Lidar Inertial Odometry (LIO), which synthesizes the point-wise point-to-plane match and map refreshment into a probabilistic model. As a result, it can address the issue of mismatching during point registration and remove in-frame motion distortion of Lidar sensors. Specifically, the uncertainty-aware map is designed to embody the uncerta...Show More

MUP-LIO: Mapping Uncertainty-aware Point-wise Lidar Inertial Odometry

Hekai Yao;Xuetao Zhang;Gang Sun;Yisha Liu;Xuebo Zhang;Yan Zhuang

2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Year: 2024 | Conference Paper |

CIGF-Net: Cross-Modality Interaction and Global-Feature Fusion for RGB-T Semantic Segmentation

Zhiwei Zhang;Yisha Liu;Weimin Xue;Yan Zhuang

IEEE Transactions on Emerging Topics in Computational Intelligence

Year: 2024 | Early Access Article |

RGB-T semantic segmentation aims to enhance the robustness of segmentation methods in complex environments by utilizing thermal information. To facilitate the effective interaction and fusion of multimodal information, we propose a novel Cross-modality Interaction and Global-feature Fusion Network, namely CIGF-Net. In each feature extraction stage, we propose a Cross-modality Interaction Module (C...Show More

CIGF-Net: Cross-Modality Interaction and Global-Feature Fusion for RGB-T Semantic Segmentation

Zhiwei Zhang;Yisha Liu;Weimin Xue;Yan Zhuang

IEEE Transactions on Emerging Topics in Computational Intelligence

Year: 2024 | Early Access Article |

Nonlinear Control of a Fully-Actuated Hexacopter with a Cable-Suspended Load

Zhicheng Li;Hanzhang Wang;Xuetao Zhang;Yisha Liu;Xuebo Zhang;Yan Zhuang

2024 14th Asian Control Conference (ASCC)

Year: 2024 | Conference Paper |

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This paper introduces a novel nonlinear controller to address load transportation problems of fully-actuated un-manned aerial vehicle (UAV). Unlike existing controllers, this controller employs a layered structure, where the outer loop controls the payload and its speed, while the inner loop controls the attitude of the load. Given the full actuation of the UAV, translation and attitude are separa...Show More

Nonlinear Control of a Fully-Actuated Hexacopter with a Cable-Suspended Load

Zhicheng Li;Hanzhang Wang;Xuetao Zhang;Yisha Liu;Xuebo Zhang;Yan Zhuang

2024 14th Asian Control Conference (ASCC)

Year: 2024 | Conference Paper |

A Novel Teacher–Student Framework With Degradation Model for Infrared–Visible Image Fusion

Weimin Xue;Yisha Liu;Fei Wang;Guojian He;Yan Zhuang

IEEE Transactions on Instrumentation and Measurement

Year: 2024 | Volume: 73 | Journal Article |

Cited by: Papers (2)

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The fusion performance of infrared (IR) and visible (VIS) images depends on the quality of the source images, which are often affected by some factors in real-world scenarios, such as environmental changes, hardware limitations, and image compression. The influence of these factors can be minimized by training a neural network capable of generating high-quality (HQ) fused images from low-quality (...Show More

A Novel Teacher–Student Framework With Degradation Model for Infrared–Visible Image Fusion

Weimin Xue;Yisha Liu;Fei Wang;Guojian He;Yan Zhuang

IEEE Transactions on Instrumentation and Measurement

Year: 2024 | Volume: 73 | Journal Article |

FLM PL-VIO: A Robust Monocular Point-Line Visual-Inertial Odometry Based on Fast Line Matching

Shuyue Lin;Xuetao Zhang;Yisha Liu;Hanzhang Wang;Xuebo Zhang;Yan Zhuang

IEEE Transactions on Industrial Electronics

Year: 2024 | Volume: 71, Issue: 12 | Journal Article |

Cited by: Papers (1)

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This article proposes a monocular point-line visual-inertial odometry (VIO) with line filtering and fast line matching (FLM PL-VIO), improving the localization accuracy and robustness. Different from the existing point-line VIO frameworks, we construct a new line filter to make the line features uniformly distributed, providing better spatial geometric constraints. In addition, we propose a fast l...Show More

FLM PL-VIO: A Robust Monocular Point-Line Visual-Inertial Odometry Based on Fast Line Matching

Shuyue Lin;Xuetao Zhang;Yisha Liu;Hanzhang Wang;Xuebo Zhang;Yan Zhuang

IEEE Transactions on Industrial Electronics

Year: 2024 | Volume: 71, Issue: 12 | Journal Article |

Safety-Driven and Localization Uncertainty-Driven Perception-Aware Trajectory Planning for Quadrotor Unmanned Aerial Vehicles

Gang Sun;Xuetao Zhang;Yisha Liu;Xuebo Zhang;Yan Zhuang

IEEE Transactions on Intelligent Transportation Systems

Year: 2024 | Volume: 25, Issue: 8 | Journal Article |

Cited by: Papers (1)

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Recent advances in trajectory planning have enabled quadrotor unmanned aerial vehicles (UAVs) to navigate autonomously in complex environments. However, most of the existing methods do not consider the perception quality and the safety simultaneously. This article proposes a perception-aware trajectory planning strategy for quadrotors, which can ensure the safety and localization accuracy. In cont...Show More

Safety-Driven and Localization Uncertainty-Driven Perception-Aware Trajectory Planning for Quadrotor Unmanned Aerial Vehicles

Gang Sun;Xuetao Zhang;Yisha Liu;Xuebo Zhang;Yan Zhuang

IEEE Transactions on Intelligent Transportation Systems

Year: 2024 | Volume: 25, Issue: 8 | Journal Article |

FGIP: A Frontier-Guided Informative Planner for UAV Exploration and Reconstruction

Xuetao Zhang;Xiaotian Xu;Yisha Liu;Hanzhang Wang;Xuebo Zhang;Yan Zhuang

IEEE Transactions on Industrial Informatics

Year: 2024 | Volume: 20, Issue: 4 | Journal Article |

Cited by: Papers (4)

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This article proposes a frontier-guided informative planner for unmanned aerial vehicle volumetric exploration and 3-D reconstruction, which can explore a complex unknown environment and provide the accurate truncated signed distance function reconstruction simultaneously. Different from the existing methods, the key insight of the proposed method is that the hybrid surface frontier is proposed to...Show More

FGIP: A Frontier-Guided Informative Planner for UAV Exploration and Reconstruction

Xuetao Zhang;Xiaotian Xu;Yisha Liu;Hanzhang Wang;Xuebo Zhang;Yan Zhuang

IEEE Transactions on Industrial Informatics

Year: 2024 | Volume: 20, Issue: 4 | Journal Article |

SVPTO: Safe Visibility-Guided Perception-Aware Trajectory Optimization for Aerial Tracking

Hanzhang Wang;Xuetao Zhang;Yisha Liu;Xuebo Zhang;Yan Zhuang

IEEE Transactions on Industrial Electronics

Year: 2024 | Volume: 71, Issue: 3 | Journal Article |

Cited by: Papers (6)

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In this article, we propose a safe visibility-guided perception-aware trajectory optimization method for aerial tracking, which can handle occlusion and collision in complex environments simultaneously. Different from the existing methods, the key idea of the proposed method is to actively adjust the relative distance and angle between the quadrotor and the target to avoid occlusion and collision ...Show More

SVPTO: Safe Visibility-Guided Perception-Aware Trajectory Optimization for Aerial Tracking

Hanzhang Wang;Xuetao Zhang;Yisha Liu;Xuebo Zhang;Yan Zhuang

IEEE Transactions on Industrial Electronics

Year: 2024 | Volume: 71, Issue: 3 | Journal Article |

MAPPG: Multi-Agent Phasic Policy Gradient

Qi Zhang;Xuetao Zhang;Yisha Liu;Xuebo Zhang;Yan Zhuang

2023 62nd IEEE Conference on Decision and Control (CDC)

Year: 2023 | Conference Paper |

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We propose a Multi-Agent Phasic Policy Gradient (MAPPG) algorithm, which can assist agents to further alleviate the non-stationarity of the environment. Different from the existing methods, the auxiliary phase is introduced to train the local policy directly by using the environment state, which can be naturally integrated into other algorithms. Specifically, the hidden layer feature sharing is pr...Show More

MAPPG: Multi-Agent Phasic Policy Gradient

Qi Zhang;Xuetao Zhang;Yisha Liu;Xuebo Zhang;Yan Zhuang

2023 62nd IEEE Conference on Decision and Control (CDC)

Year: 2023 | Conference Paper |

LSR-RIBNet: A Novel LiDAR Super-Resolution Model for Scene Semantic Segmentation in Outdoor Environments

Guojian He;Yisha Liu;Qingyang Tan

2023 13th International Conference on Information Science and Technology (ICIST)

Year: 2023 | Conference Paper |

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LiDAR-based scene semantic segmentation is crucial for unmanned ground vehicles working in outdoor environments. However, the performance of scene semantic segmentation is often poor due to the low vertical resolution of commonly used LiDAR sensors. To achieve high-resolution LiDAR data for semantic segmentation tasks, this paper proposes a novel LiDAR super-resolution network named LSR-RIBNet. To...Show More

LSR-RIBNet: A Novel LiDAR Super-Resolution Model for Scene Semantic Segmentation in Outdoor Environments

Guojian He;Yisha Liu;Qingyang Tan

2023 13th International Conference on Information Science and Technology (ICIST)

Year: 2023 | Conference Paper |

Weakly Supervised Semantic Segmentation for Point Clouds with Self-Purification on Pseudo Labels

Junfeng Li;Fei Yan;Yisha Liu;Yan Zhuang

2023 13th International Conference on Information Science and Technology (ICIST)

Year: 2023 | Conference Paper |

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Currently, research in the field about point cloud semantic segmentation primarily focuses on fully supervised learning, which requires expensive manual point-level annotations. Weakly supervised learning is an approach to overcome the time and effort required for such annotations. However, for large-scale point clouds with limited labeled points, it is challenging for networks to learn the differ...Show More

Weakly Supervised Semantic Segmentation for Point Clouds with Self-Purification on Pseudo Labels

Junfeng Li;Fei Yan;Yisha Liu;Yan Zhuang

2023 13th International Conference on Information Science and Technology (ICIST)

Year: 2023 | Conference Paper |

Topology-Guided Perception-Aware Receding Horizon Trajectory Generation for UAVs

Gang Sun;Xuetao Zhang;Yisha Liu;Hanzhang Wang;Xuebo Zhang;Yan Zhuang

2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Year: 2023 | Conference Paper |

Cited by: Papers (1)

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The perception-aware motion planning method based on the localization uncertainty has the potential to improve the localization accuracy for robot navigation. How-ever, most of the existing perception-aware methods pre-build a global feature map and can not generate the perception- aware trajectory in real time. This paper proposes a topology- guided perception-aware receding horizon trajectory ge...Show More

Topology-Guided Perception-Aware Receding Horizon Trajectory Generation for UAVs

Gang Sun;Xuetao Zhang;Yisha Liu;Hanzhang Wang;Xuebo Zhang;Yan Zhuang

2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Year: 2023 | Conference Paper |

Active Informative Receding Horizon Planning for Quadrotors

Gang Sun;Xuetao Zhang;Yisha Liu;Hanzhang Wang;Yan Zhuang

2023 42nd Chinese Control Conference (CCC)

Year: 2023 | Conference Paper |

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Motion planning for quadrotors is still a challenging problem due to the uncertainty and complexity of the environment. In this context, we propose an active informative receding horizon trajectory generation method that incorporates the informative path planning and B-spline-based trajectory optimization to achieve a smooth, safe, and efficient trajectory for UAVs. The proposed method includes an...Show More

Active Informative Receding Horizon Planning for Quadrotors

Gang Sun;Xuetao Zhang;Yisha Liu;Hanzhang Wang;Yan Zhuang

2023 42nd Chinese Control Conference (CCC)

Year: 2023 | Conference Paper |

Point-Voxel Fusion for 3D Object Detection

Wei Wu;Yisha Liu;Weimin Xue;Yan Zhuang

2023 42nd Chinese Control Conference (CCC)

Year: 2023 | Conference Paper |

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In 3D object detection, network prediction accuracy is greatly affected by point cloud's feature richness. However, the feature richness depends on fine-grained features extracted by the network. Currently some methods use voxel encoding approach continuously down-scaled by 3D convolution to improve the detection efficiency, but lose too many fine-grained features. Some methods directly inputting ...Show More

Point-Voxel Fusion for 3D Object Detection

Wei Wu;Yisha Liu;Weimin Xue;Yan Zhuang

2023 42nd Chinese Control Conference (CCC)

Year: 2023 | Conference Paper |

Automatic and Targetless LiDAR–Camera Extrinsic Calibration Using Edge Alignment

Jun Yin;Fei Yan;Yisha Liu;Yan Zhuang

IEEE Sensors Journal

Year: 2023 | Volume: 23, Issue: 17 | Journal Article |

Cited by: Papers (2)

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In this article, an automatic and targetless extrinsic calibration method is proposed for the light detection and ranging (LiDAR)–camera system, which can calibrate extrinsic parameters from coarse to fine in natural scenes. The calibration method contains a motion-based stage and a feature-based stage. In the motion-based stage, LiDAR and camera motions are inferred during the localization. Also,...Show More

Automatic and Targetless LiDAR–Camera Extrinsic Calibration Using Edge Alignment

Jun Yin;Fei Yan;Yisha Liu;Yan Zhuang

IEEE Sensors Journal

Year: 2023 | Volume: 23, Issue: 17 | Journal Article |

Real-Time Semantic Segmentation of LiDAR Point Clouds on Edge Devices for Unmanned Systems

Fei Wang;Zhao Wu;Yujie Yang;Wanyu Li;Yisha Liu;Yan Zhuang

IEEE Transactions on Instrumentation and Measurement

Year: 2023 | Volume: 72 | Journal Article |

Cited by: Papers (3)

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Real-time semantic segmentation of LiDAR measurements is crucial for high-level perception in unmanned systems, such as unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs). The limited computation and memory capacity of onboard devices, however, restricts most existing methods to offline analyses. To solve this problem, this article proposes an attention-based 3-D semantic segmenta...Show More

Real-Time Semantic Segmentation of LiDAR Point Clouds on Edge Devices for Unmanned Systems

Fei Wang;Zhao Wu;Yujie Yang;Wanyu Li;Yisha Liu;Yan Zhuang

IEEE Transactions on Instrumentation and Measurement

Year: 2023 | Volume: 72 | Journal Article |

Image-Based Visual Servoing of a Fully-Actuated Hexacopter

Xuetao Zhang;Zhicheng Li;Yisha Liu;Hanzhang Wang;Xuebo Zhang;Yan Zhuang

2022 IEEE International Conference on Unmanned Systems (ICUS)

Year: 2022 | Conference Paper |

Cited by: Papers (1)

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In this paper, a novel image-based visual servoing (IBVS) approach is proposed for a fully-actuated hexacopter, which allows to maintain a stable attitude with the horizontal movement. In contrast to the existing methods, the key insight is to integrate the IBVS method to the fully-actuated hexacopter, which can alleviate the field of view (FOV) constraint. Specifically, the nonlinear hierarchical...Show More

Image-Based Visual Servoing of a Fully-Actuated Hexacopter

Xuetao Zhang;Zhicheng Li;Yisha Liu;Hanzhang Wang;Xuebo Zhang;Yan Zhuang

2022 IEEE International Conference on Unmanned Systems (ICUS)

Year: 2022 | Conference Paper |

Volumetric Instance-Level Semantic Mapping Via BlendMask

Guoyi Sun;Xuetao Zhang;Yubin Chu;Yisha Liu;Xuebo Zhang;Yan Zhuang

2022 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)

Year: 2022 | Conference Paper |

Cited by: Papers (1)

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Advanced tasks such as planning and scene interaction for autonomous robots require a detailed instance-level semantic map of the environment. To this end, this paper proposes a new volumetric instance-level semantic mapping approach, in which BlendMask is introduced as the instance segmentation algorithm for RGB images. As a result, improvements in the quality and speed of the instance segmentati...Show More

Volumetric Instance-Level Semantic Mapping Via BlendMask

Guoyi Sun;Xuetao Zhang;Yubin Chu;Yisha Liu;Xuebo Zhang;Yan Zhuang

2022 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)

Year: 2022 | Conference Paper |

A “Look-Backward-and-Forward” Adaptation Strategy for Assessing Parameter Estimation Error of Human Motion Prediction Model

Silu Chen;Yufan Zhu;Yisha Liu;Chi Zhang;Zhongyu Piao;Guilin Yang

IEEE Robotics and Automation Letters

Year: 2022 | Volume: 7, Issue: 2 | Journal Article |

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The prediction of human motion is essential for safe human-robot collaboration (HRC). For existing prediction methods based on adaptive neural network (NN) models, estimation errors (EEs) of model parameters are directly coupled with prior EEs of trajectories. This results in poor assessment of the mean square estimation error (MSEE) of model parameters, which is a potential danger to the safety H...Show More

A “Look-Backward-and-Forward” Adaptation Strategy for Assessing Parameter Estimation Error of Human Motion Prediction Model

Silu Chen;Yufan Zhu;Yisha Liu;Chi Zhang;Zhongyu Piao;Guilin Yang

IEEE Robotics and Automation Letters

Year: 2022 | Volume: 7, Issue: 2 | Journal Article |

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