SLIDER：使用滑动局部地图的稀疏历史引导空中机器人目标搜索
SLIDER: Sparse History-Guided Aerial Robot Target Search using Sliding Local Maps
AI总结：
针对空中机器人在大规模未知环境中目标搜索难题，提出SLIDER框架，结合局部滑动地图与稀疏全局历史信息，通过新评估方法、聚类策略及维护拓扑地图实现高效搜索，性能优于现有方法。
AI中文摘要：
由于对广泛空间覆盖、细粒度感知和实时决策的需求，在大规模未知环境中进行高效探索和目标搜索对空中机器人仍然具有挑战性。本文提出了SLIDER，一个轻量级且内存高效的框架，通过将局部滑动地图与稀疏全局历史信息相结合，避免依赖全局密集地图。提出了一种新颖的观测质量评估方法，利用历史姿态和传感器模型实时评估点云数据，实现高效前沿检测。为支持可扩展和响应式规划，增量视点聚类策略动态适应局部更新，显著减少候选目标数量并降低计算负载。增量维护稀疏全局拓扑地图以辅助全局规划和成本评估。广泛的模拟和实际实验表明，该系统在内存使用、决策延迟和搜索效率方面优于现有方法。
英文摘要：
Efficient exploration and target search in large-scale unknown environments remain challenging for aerial robots due to the demands of broad spatial coverage, fine-grained perception, and real-time decision-making. This paper presents SLIDER, a lightweight and memory-efficient framework that avoids reliance on globally dense maps by combining a local sliding map with sparse global history information. A novel observation quality evaluation method is proposed, leveraging historical poses and sensor models to assess point cloud data in real-time, enabling efficient frontier detection. To support scalable and responsive planning, an incremental viewpoint clustering strategy dynamically adapts to local updates, significantly reducing the number of candidate targets and decreasing computational load. A sparse global topological map is incrementally maintained to assist global planning and cost evaluation. Extensive simulations and real-world experiments demonstrate that the proposed system outperforms state-of-the-art methods in memory usage, decision latency, and search efficiency.
