IEEE Robotics and Automation Letters最新文献_第10页

Temporal Scene-Object Graph Learning for Object Navigation

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-03-19 DOI: 10.1109/LRA.2025.3553055

Lu Chen;Zongtao He;Liuyi Wang;Chengju Liu;Qijun Chen

{"title":"Temporal Scene-Object Graph Learning for Object Navigation","authors":"Lu Chen;Zongtao He;Liuyi Wang;Chengju Liu;Qijun Chen","doi":"10.1109/LRA.2025.3553055","DOIUrl":"https://doi.org/10.1109/LRA.2025.3553055","url":null,"abstract":"Object navigation tasks require agents to locate target objects within unfamiliar indoor environments. However, the first-person perspective inherently imposes limited visibility, complicating global planning. Hence, it becomes imperative for the agent to cultivate an efficient visual representation from this restricted viewpoint. To address this, we introduce a temporal scene-object graph (TSOG) to construct an informative and efficient ego-centric visual representation. Firstly, we develop a holistic object feature descriptor (HOFD) to fully describe object features from different aspects, facilitating the learning of relationships between observed and unseen objects. Next, we propose a scene-object graph (SOG) to simultaneously learn local and global correlations between objects and agent observations, granting the agent a more comprehensive and flexible scene understanding ability. This facilitates the agent to perform target association and search more efficiently. Finally, we introduce a temporal graph aggregation (TGA) module to dynamically aggregate memory information across consecutive time steps. TGA offers the agent a dynamic perspective on historical steps, aiding in navigation towards the target in longer trajectories. Extensive experiments in AI2THOR and Gibson datasets demonstrate our method's effectiveness and efficiency for ObjectNav tasks in unseen environments.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 5","pages":"4914-4921"},"PeriodicalIF":4.6,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MUSE: A Real-Time Multi-Sensor State Estimator for Quadruped Robots MUSE：四足机器人的实时多传感器状态估计器

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-03-19 DOI: 10.1109/LRA.2025.3553047

Ylenia Nisticò;João Carlos Virgolino Soares;Lorenzo Amatucci;Geoff Fink;Claudio Semini

引用次数: 0

First-Person View Interfaces for Teleoperation of Aerial Swarms

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-03-19 DOI: 10.1109/LRA.2025.3553062

Benjamin Jarvis;Charbel Toumieh;Dario Floreano

{"title":"First-Person View Interfaces for Teleoperation of Aerial Swarms","authors":"Benjamin Jarvis;Charbel Toumieh;Dario Floreano","doi":"10.1109/LRA.2025.3553062","DOIUrl":"https://doi.org/10.1109/LRA.2025.3553062","url":null,"abstract":"Aerial swarms can substantially improve the effectiveness of drones in applications such as inspection, monitoring, and search for rescue. This is especially true when those swarms are made of several individual drones that use local sensing and coordination rules to achieve collective motion. Despite recent progress in swarm autonomy, human control and decision-making are still critical for missions where lives are at risk or human cognitive skills are required. However, first-person-view (FPV) teleoperation systems require one or more human operators per drone, limiting the scalability of these systems to swarms. This work investigates the performance, preference, and behaviour of pilots using different FPV interfaces for teleoperation of aerial swarms. Interfaces with single and multiple perspectives were experimentally studied with humans piloting a simulated aerial swarm through an obstacle course. Participants were found to prefer and perform better with views from the back of the swarm, while views from the front caused users to fly faster but resulted in more crashes. Presenting users with multiple views at once resulted in a slower completion time, and users were found to focus on the largest view, regardless of its perspective within the swarm.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 5","pages":"4476-4483"},"PeriodicalIF":4.6,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

HeR-DRL:Heterogeneous Relational Deep Reinforcement Learning for Single-Robot and Multi-Robot Crowd Navigation

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-03-19 DOI: 10.1109/LRA.2025.3553050

Xinyu Zhou;Songhao Piao;Wenzheng Chi;Liguo Chen;Wei Li

{"title":"HeR-DRL:Heterogeneous Relational Deep Reinforcement Learning for Single-Robot and Multi-Robot Crowd Navigation","authors":"Xinyu Zhou;Songhao Piao;Wenzheng Chi;Liguo Chen;Wei Li","doi":"10.1109/LRA.2025.3553050","DOIUrl":"https://doi.org/10.1109/LRA.2025.3553050","url":null,"abstract":"Crowd navigation has garnered significant research attention in recent years, particularly with the advent of DRL-based methods. Current DRL-based methods have extensively explored interaction relationships in single-robot scenarios. However, the heterogeneity of multiple interaction relationships is often disregarded. This “interaction blind spot” hinders progress towards more complex scenarios, such as multi-robot crowd navigation. In this letter, we propose a heterogeneous relational deep reinforcement learning method, named HeR-DRL, which utilizes a customized heterogeneous Graph Neural Network (GNN) to enhance overall performance in crowd navigation. Firstly, we devised a method for constructing robot-crowd heterogenous relation graph that effectively simulates the heterogeneous pair-wise interaction relationships. Based on this graph, we proposed a novel heterogeneous GNN to encode interaction relationship information. Finally, we incorporate the encoded information into deep reinforcement learning to explore the optimal policy. HeR-DRL is rigorously evaluated by comparing it to state-of-the-art algorithms in both single-robot and multi-robot circle crossing scenarios. The experimental results demonstrate that HeR-DRL surpasses the state-of-the-art approaches in overall performance, particularly excelling in terms of efficiency and comfort. This underscores the significance of heterogeneous interactions in crowd navigation.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 5","pages":"4524-4531"},"PeriodicalIF":4.6,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust LiDAR-Camera Calibration With 2D Gaussian Splatting

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-03-19 DOI: 10.1109/LRA.2025.3552955

Shuyi Zhou;Shuxiang Xie;Ryoichi Ishikawa;Takeshi Oishi

引用次数: 0

RGBDS-SLAM: A RGB-D Semantic Dense SLAM Based on 3D Multi Level Pyramid Gaussian Splatting

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-03-19 DOI: 10.1109/LRA.2025.3553049

Zhenzhong Cao;Chenyang Zhao;Qianyi Zhang;Jinzheng Guang;Yinuo Song;Jingtai Liu

{"title":"RGBDS-SLAM: A RGB-D Semantic Dense SLAM Based on 3D Multi Level Pyramid Gaussian Splatting","authors":"Zhenzhong Cao;Chenyang Zhao;Qianyi Zhang;Jinzheng Guang;Yinuo Song;Jingtai Liu","doi":"10.1109/LRA.2025.3553049","DOIUrl":"https://doi.org/10.1109/LRA.2025.3553049","url":null,"abstract":"High-fidelity reconstruction is crucial for dense SLAM. Recent popular methods utilize 3D Gaussian splatting (3D GS) techniques for RGB, depth, and semantic reconstruction of scenes. However, these methods ignore issues of detail and consistency in different parts of the scene. To address this, we propose RGBDS-SLAM, a RGB-D semantic dense SLAM system based on 3D multi-level pyramid Gaussian splatting, which enables high-fidelity dense reconstruction of scene RGB, depth, and semantics. In this system, we introduce a 3D multi-level pyramid Gaussian splatting method that restores scene details by extracting multi-level image pyramids for Gaussian splatting training, ensuring consistency in RGB, depth, and semantic reconstructions. Additionally, we design a tightly-coupled multi-features reconstruction optimization mechanism, allowing the reconstruction accuracy of RGB, depth, and semantic features to mutually enhance each other during the rendering optimization process. Extensive quantitative, qualitative, and ablation experiments on the Replica and ScanNet public datasets demonstrate that our proposed method outperforms current state-of-the-art methods, which achieves great improvement by 11.13% in PSNR and 68.57% in LPIPS.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 5","pages":"4778-4785"},"PeriodicalIF":4.6,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pose Estimation of Magnetically Driven Helical Robots With Eye-in-Hand Magnetic Sensing

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-03-18 DOI: 10.1109/LRA.2025.3553048

Yong Zeng;Guangyu Chen;Haoxiang Lian;Kun Bai

{"title":"Pose Estimation of Magnetically Driven Helical Robots With Eye-in-Hand Magnetic Sensing","authors":"Yong Zeng;Guangyu Chen;Haoxiang Lian;Kun Bai","doi":"10.1109/LRA.2025.3553048","DOIUrl":"https://doi.org/10.1109/LRA.2025.3553048","url":null,"abstract":"This letter presents a magnetic-based pose sensing method for magnetically driven helical robots. Unlike conventional methods that directly compute pose from magnetic field measurements, the proposed approach decouples magnetic field components caused by the helical robot's pose from the rotating magnetic field by deriving the analytic relationship between the spatial characteristics of the rotating magnetic field and the rotating permanent magnet (PM). A magnetic field model for a dual-rotating PM system is established under quasi-static driving conditions, enabling real-time pose estimation by taking account into the effects of the driving PM. To address workspace and signal quality limitations, a mobile sensor array in eye-in-hand configuration is presented, achieving follow-up measurements with improved signal-to-noise ratio and high precision. The proposed method has been validated experimentally on a magnetically driving platform and the results demonstrate that this method enables large-range tracking with limited number of sensors and provides a robust solution for continuous real-time pose sensing for in magnetically driven helical robots.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 5","pages":"4604-4611"},"PeriodicalIF":4.6,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MuSCLe-Reg: Multi-Scale Contextual Embedding and Local Correspondence Rectification for Robust Two-Stage Point Cloud Registration MuSCLe-Reg：用于鲁棒两阶段点云注册的多尺度上下文嵌入和局部对应校正

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-03-18 DOI: 10.1109/LRA.2025.3551954

Yangyang Zhang;Jialong Zhang;Xiaolong Qian;Yi Cen;Bowen Zhang;Jun Gong

{"title":"MuSCLe-Reg: Multi-Scale Contextual Embedding and Local Correspondence Rectification for Robust Two-Stage Point Cloud Registration","authors":"Yangyang Zhang;Jialong Zhang;Xiaolong Qian;Yi Cen;Bowen Zhang;Jun Gong","doi":"10.1109/LRA.2025.3551954","DOIUrl":"https://doi.org/10.1109/LRA.2025.3551954","url":null,"abstract":"Algorithm of outlier removal for learning-based 3D point cloud registration is usually regarded as a classification problem. The core for this to be successful is to learn the discriminative inlier/outlier feature representations. This letter proposes a two-stage efficient network (MuSCLe-Reg) with multi-scale local feature fusion embedding. Specifically, we have designed a two-stage registration architecture. Firstly, we construct a graph topology feature consisting of correspondences and their feature neighborhoods. Then, the feature representation of correspondences was enhanced through multi-scale feature mapping and fusion (MSF). In addition, we propose a local correspondence rectification strategy (LCR) based on feature neighbors to evaluate initial candidates and generate higher-quality correspondences. The experimental results on various datasets show that compared with existing learning-based algorithms, this network has better accuracy and stronger generalization ability. Especially, in robustness testing across varying numbers of correspondences in the 3DLoMatch dataset, the algorithm demonstrated superior estimation performance compared to current state-of-the-art registration techniques.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 5","pages":"4754-4761"},"PeriodicalIF":4.6,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Information-Theoretic Detection of Bimanual Interactions for Dual-Arm Robot Plan Generation

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-03-17 DOI: 10.1109/LRA.2025.3552216

Elena Merlo;Marta Lagomarsino;Arash Ajoudani

引用次数: 0

3D Single Object Tracking With Cross-Modal Fusion Conflict Elimination

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-03-17 DOI: 10.1109/LRA.2025.3551951

Yushi Yang;Wei Li;Ying Yao;Bo Zhou;Baojie Fan

{"title":"3D Single Object Tracking With Cross-Modal Fusion Conflict Elimination","authors":"Yushi Yang;Wei Li;Ying Yao;Bo Zhou;Baojie Fan","doi":"10.1109/LRA.2025.3551951","DOIUrl":"https://doi.org/10.1109/LRA.2025.3551951","url":null,"abstract":"3D single object tracking based on point clouds is a key challenge in robotics and autonomous driving technology. Mainstream methods rely on point clouds for geometric matching or motion estimation between the target template and the search area. However, the lack of texture and the sparsity of incomplete point clouds make it difficult for unimodal trackers to distinguish objects with similar structures. To overcome the limitations of previous methods, this letter proposes a cross-modal fusion conflict elimination tracker (CCETrack). The point clouds collected by LiDAR provide accurate depth and shape information about the surrounding environment, while the camera sensor provides RGB images containing rich semantic and texture information. CCETrack fully leverages both modalities to track 3D objects. Specifically, to address cross-modal conflicts caused by heterogeneous sensors, we propose a global context alignment module that aligns RGB images with point clouds and generates enhanced image features. Then, a sparse feature enhancement module is designed to optimize voxelized point cloud features using the rich image features. In the feature fusion stage, both modalities are converted into BEV features, with the template and search area features fused separately. A self-attention mechanism is employed to establish bidirectional communication between regions. Our method maximizes the use of effective information and achieves state-of-the-art performance on the KITTI and nuScenes datasets through multimodal complementarity.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 5","pages":"4826-4833"},"PeriodicalIF":4.6,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0