多模态信息融合

Elastic Tactile Simulation Towards Tactile-Visual Perception

Yikai Wang, Wenbing Huang, Bin Fang, Fuchun Sun, Chang Li

专题命中 机器人多传感器融合 ：information fusion(abstract)；分类 cs.CV、cs.RO

Comments ACMMM 2021 (Oral). Code available at https://github.com/yikaiw/EIP. arXiv admin note: substantial text overlap with arXiv:2011.11528

AcousticFusion: Fusing Sound Source Localization to Visual SLAM in Dynamic Environments

Tianwei Zhang, Huayan Zhang, Xiaofei Li, Junfeng Chen, Tin Lun Lam, Sethu Vijayakumar

专题命中 机器人多传感器融合 ：audio-visual fusion(abstract)；分类 cs.CV、cs.RO

Comments Accepted by IROS-2021

Multi-Modality Task Cascade for 3D Object Detection

Jinhyung Park, Xinshuo Weng, Yunze Man, Kris Kitani

专题命中 机器人多传感器融合 ：sensor fusion(abstract)；分类 cs.CV、cs.RO

Fusion-DHL: WiFi, IMU, and Floorplan Fusion for Dense History of Locations in Indoor Environments

Sachini Herath, Saghar Irandoust, Bowen Chen, Yiming Qian, Pyojin Kim, Yasutaka Furukawa

专题命中 机器人多传感器融合 ：sensor fusion(abstract)；分类 cs.CV、cs.RO

Comments To be published in ICRA 2021. Code and data: https://github.com/Sachini/Fusion-DHL

Journal ref ICRA 2021

RADIATE: A Radar Dataset for Automotive Perception in Bad Weather

Marcel Sheeny, Emanuele De Pellegrin, Saptarshi Mukherjee, Alireza Ahrabian, Sen Wang, Andrew Wallace

专题命中 机器人多传感器融合 ：sensor fusion(abstract)；分类 cs.CV、cs.RO

Comments Accepted at IEEE International Conference on Robotics and Automation 2021 (ICRA 2021)

Transmitter Discovery through Radio-Visual Probabilistic Active Sensing

Luca Varotto, Angelo Cenedese

专题命中 机器人多传感器融合 ：sensor fusion(abstract)；分类 eess.SP、cs.RO

Comments 7 pages, 4 figures, 1 table, submitted to MMAR 2021

IMU Data Processing For Inertial Aided Navigation: A Recurrent Neural Network Based Approach

Ming Zhang, Mingming Zhang, Yiming Chen, Mingyang Li

专题命中 机器人多传感器融合 ：sensor fusion(abstract)；分类 cs.CV、cs.RO

Comments IEEE International Conference on Robotics and Automation (ICRA), Xi'an, China, 2021

Demo Abstract: Indoor Positioning System in Visually-Degraded Environments with Millimetre-Wave Radar and Inertial Sensors

Zhuangzhuang Dai, Muhamad Risqi U. Saputra, Chris Xiaoxuan Lu, Niki Trigoni, Andrew Markham

专题命中 机器人多传感器融合 ：sensor fusion(abstract)；分类 cs.CV、cs.RO

Comments Appear as demo abstract at the ACM Conference on Embedded Networked Sensor Systems (SenSys 2020)

A Multisensory Learning Architecture for Rotation-invariant Object Recognition

Murat Kirtay, Guido Schillaci, Verena V. Hafner

专题命中 机器人多传感器融合 ：multimodal fusion(abstract)；分类 cs.CV、cs.RO

Comments The manuscript consists of 8 pages with 6 figures and two results tables. Additionally, we provide a dedicated website to reach the dataset that we employed for this study: http://www.robotmultimodal.com/datasets/

Improvements to Target-Based 3D LiDAR to Camera Calibration

Jiunn-Kai Huang, Jessy W. Grizzle

专题命中 机器人多传感器融合 ：sensor fusion(abstract)；分类 cs.CV、cs.RO

Journal ref IEEE Access, vol. 8, 2020, pp. 134101-134110

SelfVIO: Self-Supervised Deep Monocular Visual-Inertial Odometry and Depth Estimation

Yasin Almalioglu, Mehmet Turan, Alp Eren Sari, Muhamad Risqi U. Saputra, Pedro P. B. de Gusmão, Andrew Markham, Niki Trigoni

专题命中 机器人多传感器融合 ：sensor fusion(abstract)；分类 cs.CV、eess.IV

Comments 15 pages, submitted to The IEEE Transactions on Robotics (T-RO) journal, under review

TEX-CUP: The University of Texas Challenge for Urban Positioning

Lakshay Narula, Daniel M. LaChapelle, Matthew J. Murrian, J. Michael Wooten, Todd E. Humphreys, Elliot de Toldi, Guirec Morvant, Jean-Baptiste Lacambre

专题命中 机器人多传感器融合 ：sensor fusion(abstract)；分类 eess.SP、cs.RO

Comments 8 pages, 6 figures. To appear in IEEE/ION Position, Location, and Navigation Symposium (PLANS), 2020

Learning Pose Estimation for UAV Autonomous Navigation andLanding Using Visual-Inertial Sensor Data

Francesca Baldini, Animashree Anandkumar, Richard M. Murray

专题命中 机器人多传感器融合 ：multimodal fusion(abstract)；分类 cs.CV、cs.RO

Incrementally Learned Mixture Models for GNSS Localization

Tim Pfeifer, Peter Protzel

专题命中 机器人多传感器融合 ：sensor fusion(abstract)；分类 eess.SP、cs.RO

Comments 8 pages, 5 figures, published in proceedings of IEEE Intelligent Vehicles Symposium (IV) 2019

WiFi-Inertial Indoor Pose Estimation for Micro Aerial Vehicles

Shengkai Zhang, Wei Wang, Tao Jiang

专题命中 机器人多传感器融合 ：sensor fusion(abstract)；分类 eess.SP、cs.RO

Comments To appear in IEEE Transactions on Industrial Electronics

Direct Visual-Inertial Odometry with Semi-Dense Mapping

Wenju Xu, Dongkyu Choi, Guanghui Wang

专题命中 机器人多传感器融合 ：sensor fusion(abstract)；分类 cs.CV、cs.RO

Tightly Coupled 3D Lidar Inertial Odometry and Mapping

Haoyang Ye, Yuying Chen, Ming Liu

专题命中 机器人多传感器融合 ：sensor fusion(abstract)；分类 cs.CV、cs.RO

Comments Accepted by ICRA 2019

Short-Term Prediction and Multi-Camera Fusion on Semantic Grids

Lukas Hoyer, Patrick Kesper, Anna Khoreva, Volker Fischer

专题命中 机器人多传感器融合 ：sensor fusion(abstract)；分类 cs.CV、cs.RO

Multi-Modal Obstacle Detection in Unstructured Environments with Conditional Random Fields

Mikkel Kragh, James Underwood

专题命中 机器人多传感器融合 ：multimodal fusion(abstract)；分类 cs.CV、cs.RO

Comments This is the accepted version of the following article: Kragh M, Underwood J. Multimodal obstacle detection in unstructured environments with conditional random fields. J Field Robotics. 2019, 1-20., which has been published in final form at https://doi.org/10.1002/rob.21866

Capturing Object Detection Uncertainty in Multi-Layer Grid Maps

Sascha Wirges, Marcel Reith-Braun, Martin Lauer, Christoph Stiller

专题命中 机器人多传感器融合 ：sensor fusion(abstract)；分类 cs.CV、cs.RO

Comments 8 pages, 8 figures, 2 tables

Closed-form solution to cooperative visual-inertial structure from motion

Agostino Martinelli

专题命中 机器人多传感器融合 ：sensor fusion(abstract)；分类 cs.CV、cs.RO

Towards Visual Ego-motion Learning in Robots

Sudeep Pillai, John J. Leonard

专题命中 机器人多传感器融合 ：sensor fusion(abstract)；分类 cs.CV、cs.RO

Comments Conference paper; Submitted to IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2017, Vancouver CA; 8 pages, 8 figures, 2 tables

Distributed Mapping with Privacy and Communication Constraints: Lightweight Algorithms and Object-based Models

Siddharth Choudhary, Luca Carlone, Carlos Nieto, John Rogers, Henrik I. Christensen, Frank Dellaert

专题命中 机器人多传感器融合 ：sensor fusion(abstract)；分类 cs.CV、cs.RO

Comments preprint for IJRR submission

Tracking Human-like Natural Motion Using Deep Recurrent Neural Networks

Youngbin Park, Sungphill Moon, Il Hong Suh

专题命中 机器人多传感器融合 ：sensor fusion(abstract)；分类 cs.CV、cs.RO

Comments submitted to ECCV 2016

Inertial Odometry on Handheld Smartphones

Arno Solin, Santiago Cortes, Esa Rahtu, Juho Kannala

专题命中 机器人多传感器融合 ：information fusion(abstract,comments)；分类 cs.CV

Comments Appearing in Proceedings of the International Conference on Information Fusion (FUSION 2018)

SoftVTBench: A Deformation-Aware Visuo-Tactile Dataset and Benchmark for Deformable-Object Manipulation

SoftVTBench：面向可变形物体操作的形变感知视觉-触觉数据集与基准

Bowen Jing, Mingxin Wang, Ruiyang Hao, Chenchen Ge, Hanwen Shen, Junjie He, Yang Cui, Yiming Hou, Weitao Zhou, Jiawei Wang, Minglei Li, Dandan Zhang, Ding Zhao, Houde Liu, Xiaofan Li, Si Liu, Ping Luo, Haibao Yu

机构 * Tuojing Intelligence（拓境智能） ； Tsinghua University（清华大学） ； Southeast University（东南大学） ； Stevens Institute of Technology（斯蒂文斯理工学院） ； The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）） ； University of Manchester（曼彻斯特大学） ； Simple AI ； Imperial College London（帝国理工学院） ； Carnegie Mellon University（卡内基梅隆大学） ； Zhejiang University（浙江大学） ； Beihang University（北京航空航天大学） ； The University of Hong Kong（香港大学） 纠错

专题命中 机器人多传感器融合 ：multimodal fusion(abstract)；分类 cs.RO

AI总结 本研究推出SoftVTBench视觉-触觉数据集与基准，定义形变感知成功率（DSR），发现触觉信息本身未必提升多模态融合，为可变形物体操作的物理交互研究提供资源。

Learning When to See and When to Feel: Adaptive Vision-Torque Fusion for Contact-Aware Manipulation

学习何时视觉何时触觉：适应性视觉-扭矩融合用于接触感知操控

Jiuzhou Lei, Chang Liu, Yu She, Xiao Liang, Minghui Zheng

机构 * Edwardson School of Industrial Engineering, Purdue University（普渡大学爱德华森工业工程学院） ； Zachry Department of Civil and Environmental Engineering, Texas A&M University（德州农工大学扎克里土木与环境工程系） 纠错

专题命中 机器人多传感器融合 ：multimodal fusion(abstract)；分类 cs.RO

AI总结 本文提出适应性视觉-扭矩融合策略，通过比较不同融合方法提升机器人操控性能，实验显示方法在成功率上优于基线14%。

Dual IMU System for Accurate Gastrointestinal Motility Tracking

用于精确胃肠道动力跟踪的双IMU系统

Ziyao Zhou, Boyan Li, Chenyang Ren, Tiancheng Cao, Dawei Wang, Hen-Wei Huang

专题命中 机器人多传感器融合 ：sensor fusion(abstract)；分类 eess.SP

AI总结 该研究针对单IMU系统易受伪影干扰的问题，提出双IMU系统，结合传感器融合算法可有效区分胃肠道动力与身体运动，实现精确胃肠道动力跟踪。

Cross-Branch Conflict as a Shield: Safeguarding Facial Identities in Unified Multimodal Image Editing

跨分支冲突作为一种保护手段：在统一多模态图像编辑中保护面部身份

Weiwei Tan, Junxian Li, Rui Wang, Zhenhua Xu, Yanjun Zhang, Yu Leo Zhang

专题命中 机器人多传感器融合 ：multimodal fusion(abstract)；分类 cs.CV

AI总结 研究统一多模态模型中个人肖像编辑的安全问题，提出CCS统一对抗保护框架，通过联合驱动ViT和VAE表示及破坏跨分支兼容性，防止模型恢复身份信息，在抑制身份保留编辑上表现优于现有方法。

Comments 14 pages, preprint

Hierarchical and Multimodal Data for Daily Activity Understanding

层级化和多模态数据用于日常活动理解

Ghazal Kaviani, Yavuz Yarici, Seulgi Kim, Mohit Prabhushankar, Ghassan AlRegib, Mashhour Solh, Ameya Patil

机构 * Georgia Institute of Technology（佐治亚理工学院） ； Amazon Lab126（亚马逊Lab126） 纠错

专题命中 机器人多传感器融合 ：sensor fusion(abstract)；分类 cs.CV

AI总结 DARai数据集通过多模态和层级注释，旨在理解现实环境中的人类活动，支持单模态和多模态传感器融合实验，揭示日常活动理解中的挑战。

Comments Accepted for publication in DMLR