针对月球探测中地形不确定性问题，提出自适应探索规划框架，提升导航安全与地图可靠性。
CUTE-Planner: Confidence-aware Uneven Terrain Exploration Planner
- 融合卡尔曼滤波估计高程置信度，动态更新地形可通行性与不确定性
- 在模拟月面实验中实现69%不确定性降低，任务成功率从0%提升至100%
- 适合需要高可靠地图的行星探测任务，尤其适用于复杂地貌区域
行星探测机器人需在不规则地形上导航并构建可靠地图。现有方法虽考虑可通行性约束，但难以处理陨石坑等复杂地貌附近高不确定性的高程估计，且缺乏降低不确定性的探索策略，未充分考虑高程不确定性对导航安全与地图质量的影响。为此，本文提出集成安全路径生成、自适应置信度更新与置信度感知探索策略的框架。基于卡尔曼滤波的高程估计生成地形可通行性与置信度评分，并将其融入基于图的探索规划器（GBP），优先探索可通行但低置信度区域。通过使用新提出的低置信度区域占比指标，在模拟月面实验中，相较基线GBP实现69%的不确定性降低；任务成功率由0%提升至100%，显著提升探索安全性与地图可靠性。
原文摘要 · Abstract (English)
Planetary exploration robots must navigate uneven terrain while building reliable maps for space missions. However, most existing methods incorporate traversability constraints but may not handle high uncertainty in elevation estimates near complex features like craters, do not consider exploration strategies for uncertainty reduction, and typically fail to address how elevation uncertainty affects navigation safety and map quality. To address the problems, we propose a framework integrating safe path generation, adaptive confidence updates, and confidence-aware exploration strategies. Using Kalman-based elevation estimation, our approach generates terrain traversability and confidence scores, then incorporates them into Graph-Based exploration Planner (GBP) to prioritize exploration of traversable low-confidence regions. We evaluate our framework through simulated lunar experiments using a novel low-confidence region ratio metric, achieving 69% uncertainty reduction compared to baseline GBP. In terms of mission success rate, our method achieves 100% while baseline GBP achieves 0%, demonstrating improvements in exploration safety and map reliability.
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