用视觉前沿与确定性采样结合，提升无人机3D环境探索速度
FSMP: A Frontier-Sampling-Mixed Planner for Fast Autonomous Exploration of Complex and Large 3-D Environments
- 融合视觉前沿检测与确定性采样构建增量路径图
- 两阶段规划实现全局最优路径计算与平滑优化
- 在仿真与实测中均显著提升探索效率与覆盖率
本文提出一种面向复杂大尺度三维环境的快速自主探索系统框架，采用微型飞行器（MAVs）进行探索。核心思路是将基于前沿与基于采样的策略有机融合，实现环境的快速全局探索。具体而言，设计了一种基于视场（FOV）的前沿检测器，具备完备性与可靠性，用于识别三维地图中的前沿区域。不同于随机采样方法，采用确定性采样技术，结合记录的传感器视场与新发现的前沿，构建并维护一个增量式路径图。基于该路径图，提出一种两阶段路径规划方法：首先利用懒惰评估策略快速计算路径图上的全局最优探索路径；随后对最佳路径进行平滑处理，进一步提升探索效率。通过仿真与真实场景实验验证，结果表明该规划器在探索效率、计算时间与覆盖体积方面均表现出色。
原文摘要 · Abstract (English)
In this paper, we propose a systematic framework for fast exploration of complex and large 3-D environments using micro aerial vehicles (MAVs). The key insight is the organic integration of the frontier-based and sampling-based strategies that can achieve rapid global exploration of the environment. Specifically, a field-of-view-based (FOV) frontier detector with the guarantee of completeness and soundness is devised for identifying 3-D map frontiers. Different from random sampling-based methods, the deterministic sampling technique is employed to build and maintain an incremental road map based on the recorded sensor FOVs and newly detected frontiers. With the resulting road map, we propose a two-stage path planner. First, it quickly computes the global optimal exploration path on the road map using the lazy evaluation strategy. Then, the best exploration path is smoothed for further improving the exploration efficiency. We validate the proposed method both in simulation and real-world experiments. The comparative results demonstrate the promising performance of our planner in terms of exploration efficiency, computational time, and explored volume.
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