多模态信息融合

Multi-sensor fusion for fine-guidance and milliarcsecond-level attitude estimation of balloon-borne telescope

用于球载望远镜精引导及毫角秒级姿态估计的多传感器融合技术

Philippe Voyer, Maya Amit, Steven J. Benton, Benjamin E. Boyd, Anthony M. Brown, Giulia Cerini, Paul Clark, Matthew Craigie, Christopher J. Damaren, Tim Eifler, Spencer W. Everett, Aurelien A. Fraisse, Leo W. H. Fung, Ajay S. Gill, Suren Gourapura, Eric Habjan, John W. Hartley, David Harvey, Bradley Holder, Eric M. Huff, Mathilde Jauzac, William C. Jones, David Lagattuta, Gavin Leroy, Jason S. -Y. Leung, Lun Li, Thuy Vy T. Luu, Richard Massey, Jacqueline E. McCleary, Adyn Miles, Johanna M. Nagy, C. Barth Netterfield, Emaad Paracha, Susan F. Redmond, Jason D. Rhodes, Andrew Robertson, L. Javier Romualdez, Sayan Saha, Jürgen Schmoll, Mohamed M. Shaaban, Ellen Sirks, Sut Ieng Tam, Simon Tartakovsky, Georgios N. Vassilakis, André Z. Vitorelli, Bryce Warren, Alfredo Zenteno

专题命中 机器人多传感器融合 ：sensor fusion(title)；multi-sensor fusion(title)

AI总结 本研究针对球载望远镜的滚动泄漏问题，基于SuperBIT飞行数据开发仿真框架，对比单星与多星引导架构，发现多星估计可显著降低滚动引发的图像运动，为GigaBIT的多星精引导架构研究提供支撑。

Comments 21 pages, 10 figures, SPIE Astronomical Telescopes + Instrumentation 2026

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