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English(EN) EgoNeMo: Transferable Map of Pedestrian Dynamics via Egocentric LiDAR Scan

EgoNeMo 框架使用 LiDAR 构建可迁移的行人运动地图

研究人员开发了 EgoNeMo,这是一个使用以自我为中心的 3D LiDAR 扫描创建可迁移动力学地图 (MoD) 的新颖框架。该方法解决了传统 MoD 方法的局限性,即需要在每个新地点收集大量数据。EgoNeMo 采用神经隐式建模和位置平衡采样策略,以从稀疏数据泛化到未知环境。该框架还结合了多任务学习架构和面向可见性的损失函数,以改进运动分布预测并补偿不完整的观测,最终提高下游轨迹预测的可靠性。 AI

影响 在先前未绘制地图的环境中,能够实现更鲁棒的行人轨迹预测和机器人导航。

排序理由 该集群包含一篇详细介绍新框架和方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

EgoNeMo 框架使用 LiDAR 构建可迁移的行人运动地图

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该集群包含一篇详细介绍新框架和方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Azusa Sawada, Allan Wang, Hideo Saito, Aaron Steinfeld ·

    EgoNeMo:通过以自我为中心的 LiDAR 扫描实现可迁移的行人动态地图

    arXiv:2609.06195v1 Announce Type: new Abstract: This paper proposes a transferable Map of Dynamics (MoD) framework that generalizes to unknown environments using only egocentric 3D LiDAR point clouds to overcome the long-standing limitation of traditional MoD methods. While MoDs …