基于局部子图的 MAV 自主导航框架，支持激光雷达与深度相机，实现大范围高精度探索。
Efficient Submap-based Autonomous MAV Exploration using Visual-Inertial SLAM Configurable for LiDARs or Depth Cameras
- 通过局部子图与回环校正保持全局一致性，抑制状态估计漂移。
- 从子图边界高效生成全局前沿点，结合采样规划器提升探索效率。
- 兼容激光雷达与深度相机，适用于多种微型飞行器平台。
自主探索未知空间是移动机器人在真实世界部署的关键能力。安全导航需依赖精确且一致的环境地图，而机载状态估计随时间易产生漂移。本文提出一种基于局部子图的微型飞行器（MAV）探索框架，通过回环闭合校正子图间相对位姿，维持全局一致性。为支持大规模探索，方法从局部子图前沿高效计算全局环境前沿，并采用采样式最优视角规划器。该框架可无缝适配激光雷达或深度相机，适用于不同类型的MAV平台。我们在仿真中对比了当前最先进的子图探索框架，验证了本方法在效率和重建质量上的优势；同时在真实场景中展示了其有效性：分别使用搭载激光雷达和深度相机的MAV完成探索任务。视频见 https://youtu.be/Uf5fwmYcuq4。
原文摘要 · Abstract (English)
Autonomous exploration of unknown space is an essential component for the deployment of mobile robots in the real world. Safe navigation is crucial for all robotics applications and requires accurate and consistent maps of the robot's surroundings. To achieve full autonomy and allow deployment in a wide variety of environments, the robot must rely on on-board state estimation which is prone to drift over time. We propose a Micro Aerial Vehicle (MAV) exploration framework based on local submaps to allow retaining global consistency by applying loop-closure corrections to the relative submap poses. To enable large-scale exploration we efficiently compute global, environment-wide frontiers from the local submap frontiers and use a sampling-based next-best-view exploration planner. Our method seamlessly supports using either a LiDAR sensor or a depth camera, making it suitable for different kinds of MAV platforms. We perform comparative evaluations in simulation against a state-of-the-art submap-based exploration framework to showcase the efficiency and reconstruction quality of our approach. Finally, we demonstrate the applicability of our method to real-world MAVs, one equipped with a LiDAR and the other with a depth camera. Video available at https://youtu.be/Uf5fwmYcuq4 .
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