Unmanned aerial vehicles (UAVs) offer many advantages over ground vehicles, including quadruped robots, based on high maneuverability when performing exploration in complex and unknown environments. However, due to their limited computational capability, UAVs require lightweight but accurate state estimation algorithms for reliable exploration. In this paper, we propose a segmented map-based exploration system based on light detection and ranging (LiDAR)-based state estimation for UAVs. The proposed system includes capabilities such as exploration, obstacle avoidance, and object detection with localization using three-dimensional (3D) dense maps generated by tightly coupled LiDAR Inertial Odometry (LIO). Our proposed system is a hybrid system that can switch between guided and exploration modes, making it practical for search and rescue missions in disaster scenarios. The proposed LIO algorithm adapts to its surroundings, allowing for fast and accurate state estimation in complex environments. The proposed exploration algorithm is designed to cover specific regions in the 3D dense map generated by the proposed LIO, with the UAV determining if map points are included within the coverage area. We tested the proposed system in both simulation and real-world environments and validated that the proposed system outperforms state-of-the-art algorithms in various aspects such as localization accuracy and exploration efficiency in complex environments.

中文翻译：

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基于自适应激光雷达惯性里程计的自主无人机系统，用于复杂环境下的实际探索

与地面车辆（包括四足机器人）相比，无人机 (UAV) 具有许多优势，因为在复杂和未知的环境中进行探索时具有高机动性。然而，由于计算能力有限，无人机需要轻量级但准确的状态估计算法来进行可靠的探索。在本文中，我们提出了一种基于分段地图的探索系统，该系统基于基于光检测和测距（LiDAR）的无人机状态估计。所提出的系统包括探索、避障和物体检测等功能，并使用紧密耦合激光雷达惯性里程计 (LIO) 生成的三维 (3D) 密集地图进行定位。我们提出的系统是一个混合系统，可以在引导模式和探索模式之间切换，使其适用于灾难场景中的搜索和救援任务。所提出的 LIO 算法可以适应周围环境，从而可以在复杂环境中快速准确地进行状态估计。所提出的探索算法旨在覆盖所提出的 LIO 生成的 3D 密集地图中的特定区域，并由无人机确定地图点是否包含在覆盖区域内。我们在模拟和现实环境中测试了所提出的系统，并验证了所提出的系统在复杂环境中的定位精度和探索效率等各个方面都优于最先进的算法。