StepNav: 为高效且多模态视觉导航引入结构化轨迹先验
StepNav: Structured Trajectory Priors for Efficient and Multimodal Visual Navigation
AI总结：
StepNav通过结构化多模态轨迹先验提升视觉导航的鲁棒性、效率和安全性。
摘要：
视觉导航是自主系统的基础，但生成可靠轨迹在杂乱和不确定的环境中仍然是核心挑战。最近的生成模型承诺端到端合成，但它们对无结构噪声先验的依赖往往会产生不安全、低效或单模态的计划，无法满足实时要求。我们提出了StepNav，一种新的框架，通过引入源自变分原理的结构化、多模态轨迹先验来弥合这一差距。StepNav首先学习一个几何感知的成功概率场，以识别所有可行的导航走廊。这些走廊随后被用来构建一个显式的、多模态的混合先验，以初始化一个条件流匹配过程。这种细化被公式化为一个具有显式平滑性和安全性的最优控制问题。通过用物理上可行的候选者替代无结构噪声，StepNav在显著更少的步骤中生成更安全和高效的计划。在模拟和现实世界基准测试中的实验表明，与最先进的生成规划器相比，StepNav在鲁棒性、效率和安全性方面都表现出一致的改进，推动了可靠轨迹生成在实际自主导航中的应用。代码已发布在https://github.com/LuoXubo/StepNav。
英文摘要：
Visual navigation is fundamental to autonomous systems, yet generating reliable trajectories in cluttered and uncertain environments remains a core challenge. Recent generative models promise end-to-end synthesis, but their reliance on unstructured noise priors often yields unsafe, inefficient, or unimodal plans that cannot meet real-time requirements. We propose StepNav, a novel framework that bridges this gap by introducing structured, multimodal trajectory priors derived from variational principles. StepNav first learns a geometry-aware success probability field to identify all feasible navigation corridors. These corridors are then used to construct an explicit, multi-modal mixture prior that initializes a conditional flow-matching process. This refinement is formulated as an optimal control problem with explicit smoothness and safety regularization. By replacing unstructured noise with physically-grounded candidates, StepNav generates safer and more efficient plans in significantly fewer steps. Experiments in both simulation and real-world benchmarks demonstrate consistent improvements in robustness, efficiency, and safety over state-of-the-art generative planners, advancing reliable trajectory generation for practical autonomous navigation. The code has been released at https://github.com/LuoXubo/StepNav.
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。
