Visual navigation is a core capability for mobile robots, yet end-to-end learning-based methods often struggle with generalization and safety in unseen, cluttered, or narrow environments. These limitations are especially pronounced in dense indoor settings, where collisions are likely and end-to-end models frequently fail. To address this, we propose SaferPath, a hierarchical visual navigation framework that leverages learned guidance from existing end-to-end models and refines it through a safety-constrained optimization-control module. SaferPath transforms visual observations into a traversable-area map and refines guidance trajectories using Model Predictive Stein Variational Evolution Strategy (MP-SVES), efficiently generating safe trajectories in only a few iterations. The refined trajectories are tracked by an MPC controller, ensuring robust navigation in complex environments. Extensive experiments in scenarios with unseen obstacles, dense unstructured spaces, and narrow corridors demonstrate that SaferPath consistently improves success rates and reduces collisions, outperforming representative baselines such as ViNT and NoMaD, and enabling safe navigation in challenging real-world settings.

翻译：视觉导航是移动机器人的核心能力，然而端到端学习方法在未见、杂乱或狭窄环境中常面临泛化性与安全性不足的问题。这些局限在密集室内环境中尤为突出，碰撞风险高且端到端模型易失效。为此，我们提出SaferPath——一种分层视觉导航框架，该框架利用现有端到端模型的学习引导，并通过安全约束的优化控制模块对其进行精细化处理。SaferPath将视觉观测转换为可通行区域地图，并采用模型预测Stein变分进化策略（MP-SVES）优化引导轨迹，仅需数次迭代即可高效生成安全轨迹。优化后的轨迹由模型预测控制器进行跟踪，确保在复杂环境中的鲁棒导航。通过在包含未知障碍物、密集非结构化空间及狭窄走廊场景中的大量实验证明，SaferPath能持续提升成功率并降低碰撞率，其性能优于ViNT和NoMaD等代表性基线方法，实现了在具有挑战性的真实场景中的安全导航。