多模态信息融合 - arXivDaily 专题

2509.15219 2026-03-30 cs.CV cs.LG cs.MA cs.MM cs.RO 78%

Out-of-Sight Embodied Agents: Multimodal Tracking, Sensor Fusion, and Trajectory Forecasting

视线外的具身智能体：多模态跟踪、传感器融合与轨迹预测

Haichao Zhang, Yi Xu, Yun Fu

机构 * Department of Electrical and Computer Engineering, Northeastern University（东北大学电气与计算机工程系）； Khoury College of Computer Sciences, Northeastern University（东北大学库里计算机科学学院）

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

AI总结本文提出改进的视线外轨迹预测方法，通过视觉定位去噪模块提升轨迹预测性能，实验表明在Vi-Fi和JRDB数据集上达到最优效果，为自动驾驶、机器人和监控提供新方向。

Comments Published in IEEE Transactions on Pattern Analysis and Machine Intelligence (Early Access), pp. 1-14, March 23, 2026

Journal ref IEEE Transactions on Pattern Analysis and Machine Intelligence, 2026

1. 机器人多传感器融合 1 篇

Out-of-Sight Embodied Agents: Multimodal Tracking, Sensor Fusion, and Trajectory Forecasting