融合多传感器实现室内无人机高精度自主导航
Sensor Fusion for Autonomous Indoor UAV Navigation in Confined Spaces
- 结合深度相机、IMU与激光雷达数据，通过ROS与RTAB-Map实现融合感知
- 定位误差低至0.4米，地图重建RMSE为0.13米，姿态误差仅0.1%
- 适合需要高精度定位的室内巡检、搜救等场景
本文针对在未知室内封闭空间中使用自主飞行机器人导航的挑战，提出一种基于多传感器融合的解决方案。系统集成ZED 2i相机的深度感知、惯性测量单元（IMU）数据及激光雷达（LiDAR）测量，借助机器人操作系统（ROS）与RTAB-Map实现数据融合。通过定制实验验证，该方法展现出优异的鲁棒性与有效性：定位误差最低达0.4米，地图重建的均方根误差（RMSE）仅为0.13米；飞行测试表明，系统在维持期望飞行姿态方面表现精准，误差率仅为0.1%。该方案同时兼顾能效与资源平衡分配，有效解决了无人机应用中的关键问题。研究成果为无卫星信号环境下的室内自主导航提供了有力支持，适用于搜救、设施巡检与环境监测等任务。
原文摘要 · Abstract (English)
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.
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