让机器人提前预判死胡同并自动找路，提升导航安全性与效率
DR. Nav: Semantic-Geometric Representations for Proactive Dead-End Recovery and Navigation
- 用视觉与激光融合数据生成实时语义代价地图，预测死胡同风险
- 相比现有方法，检测准确率提升83.33%，到达目标时间减少52.4%
- 适合复杂未建图环境中的自主导航，如室内、户外杂乱区域
我们提出 DR. Nav（死胡同恢复感知导航），一种在无结构环境中需处理死角、植被遮挡和通道阻塞等场景下的自主导航新方法。该方法通过统一的主动策略，在无需先验假设的情况下实现对死胡同的预测与恢复。其核心是构建一个连续的、实时更新的语义代价地图，融合跨模态RGB-LiDAR信息，并采用注意力过滤机制估计每个栅格的死胡同可能性及恢复点，结合贝叶斯推理持续优化。不同于仅编码可通行性的传统建图方法，DR. Nav 显式将恢复性风险纳入代价地图，使机器人能提前识别高危区域并规划更安全路径。我们在多个密集的室内外场景中评估该方法，结果显示相较 DWA、MPPI 和 Nav2 DWB 等先进规划器，检测准确率提升 83.33%，到达目标时间减少 52.4%。
原文摘要 · Abstract (English)
We present DR. Nav (Dead-End Recovery-aware Navigation), a novel approach to autonomous navigation in scenarios where dead-end detection and recovery are critical, particularly in unstructured environments where robots must handle corners, vegetation occlusions, and blocked junctions. DR. Nav introduces a proactive strategy for navigation in unmapped environments without prior assumptions. Our method unifies dead-end prediction and recovery by generating a single, continuous, real-time semantic cost map. Specifically, DR. Nav leverages cross-modal RGB-LiDAR fusion with attention-based filtering to estimate per-cell dead-end likelihoods and recovery points, which are continuously updated through Bayesian inference to enhance robustness. Unlike prior mapping methods that only encode traversability, DR. Nav explicitly incorporates recovery-aware risk into the navigation cost map, enabling robots to anticipate unsafe regions and plan safer alternative trajectories. We evaluate DR. Nav across multiple dense indoor and outdoor scenarios and demonstrate an increase of 83.33% in accuracy in detection, a 52.4% reduction in time-to-goal (path efficiency), compared to state-of-the-art planners such as DWA, MPPI, and Nav2 DWB. Furthermore, the dead-end classifier functions
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