SemGeoNav：一种结合语义推理与几何规划的安全引导视觉导航方法
SemGeoNav:A Safety-Guided Visual Navigation Approach with Semantic Reasoning and Geometric Planning
AI总结：
提出SemGeoNav分层视觉导航框架，融合端到端模型的高层语义推理与几何方法的可靠局部规划，实现鲁棒图像导航并显著提升避障能力，在真实机器人上优于ViNT和NoMaD。
摘要：
基于学习的视觉导航增强了语义目标到达能力。然而，由于其黑箱特性，纯端到端模型通常缺乏显式的几何约束，导致在开放环境中避障不可预测且不可靠。相反，传统几何规划器确保安全性，但难以处理高维视觉目标。为了解决这些限制，我们提出了SemGeoNav，一种新颖的分层视觉导航框架。它紧密集成了端到端模型的高层语义推理与基于几何方法的可靠局部规划能力，实现了鲁棒的基于图像的导航，同时显著改善了避障。此外，我们引入了一种时间轨迹平滑机制，以确保机器人运动连续稳定。我们在真实环境中的Unitree Go2四足机器人上评估了SemGeoNav。结果表明，SemGeoNav优于现有代表性方法（包括ViNT和NoMaD），实现了更高的成功率和更短的导航时间。
英文摘要：
Learning-based visual navigation has enhanced semantic goal-reaching capabilities. However, due to their black-box nature, purely end-to-end models often lack explicit geometric constraints, leading to unpredictable and unreliable obstacle avoidance in open environments. Conversely, traditional geometric planners ensure safety but struggle with high-dimensional visual targets. To address these limitations, we propose SemGeoNav, a novel hierarchical visual navigation framework.It tightly integrates the high-level semantic reasoning of end-to-end models with the reliable local planning ability of geometry-based methods, achieving robust image-based navigation while significantly improving obstacle avoidance. Furthermore, we introduce a temporal trajectory smoothing mechanism to ensure continuous and stable robot motion. We evaluated SemGeoNav on a Unitree Go2 quadruped robot in real-world environments. The results demonstrate that SemGeoNav outperforms existing representative methods, including ViNT and NoMaD, achieving higher success rates and shorter navigation times.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
