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English(EN) A Sparse Topometric Graph for Scalable Cross-Session Indoor Navigation

新型稀疏拓扑图大幅降低室内导航内存需求

研究人员开发了一种用于室内导航的稀疏拓扑图表示方法,与传统的点级地图表示相比,该方法显著降低了计算和内存需求。该方法使用编码局部三维几何的节点和捕捉可通行连接性的加权边,即使在场景变化或使用不同传感器的情况下,也能实现稳健的跨会话导航。实验在 Gibson 和 3D-FRONT 数据集上证明了高成功率,内存使用量减少了两个数量级以上。 AI

影响 通过降低计算开销,这种方法可以实现更高效、可扩展的室内导航系统。

排序理由 该集群包含一篇详细介绍室内导航新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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

新型稀疏拓扑图大幅降低室内导航内存需求

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该集群包含一篇详细介绍室内导航新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Eun Sun Lee, Minkyun Seo, Gwangtak Bae, Junho Kim, Seunggu Kang, Jaesik Park, Young Min Kim ·

    A Sparse Topometric Graph for Scalable Cross-Session Indoor Navigation

    arXiv:2610.31432v1 Announce Type: cross Abstract: A conventional embodied agent in an indoor environment builds a map by accumulating noisy real-world measurements and results from heavy inference to guide navigation. While the detailed measurements may differ due to scene change…