Bridge inspection is currently a labor intensive task. Utilizing unmanned aerial vehicles (UAVs) to assist in inspection tasks is a promising direction. However, enabling UAVs for autonomous inspection involves the UAV state estimation problems. Since parts of UAV sensors could be unavailable, how to estimate states via sensor fusion is the key. In this paper, we propose a tightly-coupled nonlinear optimization-based system that integrates four kinds of sensors: camera, IMU, Ultra-wideband (UWB) range measurements, and global navigation satellite system (GNSS). Due to the tightly-coupled multi-sensor fusion method and system design, the system takes the advantage of the four sensors, and can seamlessly respond to indoor and outdoor GNSS and UWB loss or reacquisition. It can effectively reduce the long-term trajectory drift and provide smooth and continuous state estimation. The experimental results show that the proposed method outperforms the state-of-the-art approaches.

桥梁检查目前是一项劳动密集型任务。利用无人机（UAV）协助检查任务是一个有前途的方向。然而，使无人机进行自主巡检涉及无人机状态估计问题。由于无人机的部分传感器可能不可用，因此如何通过传感器融合来估计状态是关键。在本文中，我们提出了一种基于紧耦合非线性优化的系统，该系统集成了四种传感器：相机、IMU、超宽带（UWB）距离测量和全球导航卫星系统（GNSS）。由于采用紧密耦合的多传感器融合方法和系统设计，该系统充分利用了四个传感器的优势，可以无缝响应室内外 GNSS 和 UWB 丢失或重新捕获。它可以有效减少长期轨迹漂移并提供平滑连续的状态估计。实验结果表明，所提出的方法优于最先进的方法。