密闭空间中自主室内无人机导航的传感器融合
Sensor Fusion for Autonomous Indoor UAV Navigation in Confined Spaces
AI总结：
提出一种融合 ZED 2i 深度、IMU 与 LiDAR 的 ROS/RTAB-Map 室内无人机导航方案，实验验证其高精度、低误差及能效表现。
摘要：
本文解决在密闭空间内使用自主空中机器人穿越未知室内环境的导航挑战。我们系统的核心在于集成关键传感器技术，包括来自 ZED 2i 相机的深度感知、IMU 数据和 LiDAR 测量，并由 Robot Operating System（ROS）和 RTAB-Map 提供支持。通过定制设计的实验，我们证明了该方法的鲁棒性和有效性。结果展示了良好的导航精度，误差低至 0.4 米；建图质量的均方根误差（RMSE）仅为 0.13 米。值得注意的是，在实现这一性能的同时，系统保持了能效和均衡的资源分配，解决了无人机应用中的一个关键问题。飞行测试进一步强调了系统维持期望飞行姿态的精度，错误率仅为 0.1%。这项工作代表了自主室内无人机导航系统发展的重要一步，在 GPS 拒止的室内环境中具有搜索与救援、设施巡检和环境监测的潜在应用。
英文摘要：
In this paper, we address the challenge of navigating through unknown indoor environments using autonomous aerial robots within confined spaces. The core of our system involves the integration of key sensor technologies, including depth sensing from the ZED 2i camera, IMU data, and LiDAR measurements, facilitated by the Robot Operating System (ROS) and RTAB-Map. Through custom designed experiments, we demonstrate the robustness and effectiveness of this approach. Our results showcase a promising navigation accuracy, with errors as low as 0.4 meters, and mapping quality characterized by a Root Mean Square Error (RMSE) of just 0.13 m. Notably, this performance is achieved while maintaining energy efficiency and balanced resource allocation, addressing a crucial concern in UAV applications. Flight tests further underscore the precision of our system in maintaining desired flight orientations, with a remarkable error rate of only 0.1%. This work represents a significant stride in the development of autonomous indoor UAV navigation systems, with potential applications in search and rescue, facility inspection, and environmental monitoring within GPS-denied indoor environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
