This paper addresses the fusion of the pseudorange/pseudorange rate observations from the global navigation satellite system and the inertial–visual simultaneous localisation and mapping (SLAM) to achieve reliable navigation of unmanned aerial vehicles. This work extends the previous work on a simulation-based study [Kim et al. (2017). Compressed fusion of GNSS and inertial navigation with simultaneous localisation and mapping. IEEE Aerospace and Electronic Systems Magazine, 32(8), 22–36] to a real-flight dataset collected from a fixed-wing unmanned aerial vehicle platform. The dataset consists of measurements from visual landmarks, an inertial measurement unit, and pseudorange and pseudorange rates. We propose a novel all-source navigation filter, termed a compressed pseudo-SLAM, which can seamlessly integrate all available information in a computationally efficient way. In this framework, a local map is dynamically defined around the vehicle, updating the vehicle and local landmark states within the region. A global map includes the rest of the landmarks and is updated at a much lower rate by accumulating (or compressing) the local-to-global correlation information within the filter. It will show that the horizontal navigation error is effectively constrained with one satellite vehicle and one landmark observation. The computational cost will be analysed, demonstrating the efficiency of the method.

中文翻译：

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压缩伪SLAM：用于无人机导航的伪距集成压缩同时定位和映射

本文致力于将全球导航卫星系统的伪距/伪距率观测与惯性视觉同时定位与制图（SLAM）融合，以实现无人机的可靠导航。这项工作扩展了之前基于模拟的研究的工作 [Kim 等人。（2017）。GNSS 和惯性导航的压缩融合，同时定位和映射。IEEE 航空航天和电子系统杂志,32(8), 22-36] 到从固定翼无人机平台收集的真实飞行数据集。该数据集包括来自视觉地标的测量值、惯性测量单元以及伪距和伪距率。我们提出了一种新颖的全源导航过滤器，称为压缩伪 SLAM，它可以以计算有效的方式无缝集成所有可用信息。在这个框架中，本地地图在车辆周围动态定义，更新区域内的车辆和本地地标状态。全局地图包括其余的地标，并通过在过滤器中累积（或压缩）局部到全局相关信息以低得多的速率进行更新。它将表明，水平导航误差是有效地约束了一个卫星飞行器和一个地标观测。