轻量级图像空间导航，兼顾效率与智能决策。
Navigating the Wild: Pareto-Optimal Visual Decision-Making in Image Space
- 在图像空间直接决策，无需构建地图
- 采用帕累托最优策略平衡多目标表现
- 适合资源受限的实时导航场景
复杂真实环境下的导航需要语义理解与自适应决策。传统无地图的反应式方法在杂乱场景中易失效，基于地图的方法需大量建图工作，学习型方案依赖大规模数据且泛化能力有限。为此，我们提出帕累托最优视觉导航，一种轻量级图像空间框架，结合数据驱动语义、帕累托最优决策与视觉伺服，实现实时导航。
原文摘要 · Abstract (English)
Navigating complex real-world environments requires semantic understanding and adaptive decision-making. Traditional reactive methods without maps often fail in cluttered settings, map-based approaches demand heavy mapping effort, and learning-based solutions rely on large datasets with limited generalization. To address these challenges, we present Pareto-Optimal Visual Navigation, a lightweight image-space framework that combines data-driven semantics, Pareto-optimal decision-making, and visual servoing for real-time navigation.
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