论文标题
使用GNSS/PDR集成通过因子图优化的轨迹平滑
Trajectory Smoothing Using GNSS/PDR Integration Via Factor Graph Optimization in Urban Canyons
论文作者
论文摘要
由于多径效应和周围建筑物的反射引起的多径（NLOS）接收，因此，城市峡谷行人的准确，平稳的全球导航卫星系统（GNSS）定位仍然是一个挑战。最近开发的基于GNSS的基于GNSS定位方法为改善城市GNSS定位的新窗口通过有效利用了从历史信息来抵制离群值测量的新窗口。不幸的是，基于FGO的GNSS独立定位在高度城市化的地区仍受到挑战。作为以前基于FGO的GNSS定位方法的扩展，本文利用了FGO中行人死亡计算模型（PDR）模型的潜力，以提高Urban Canyons中GNSS独立定位性能。具体而言，根据PDR算法的板载智能手机惯性测量单元（IMU）的原始加速度测量值估算了行人的相对运动。然后使用FGO集成了RAW GNSS伪曲线，多普勒测量值和PDR的相对运动。鉴于大多数情况下的行人导航的背景较小，因此提出了一种新型的软运动模型，以使涉及因子图模型的状态平滑。通过使用智能手机级别的GNSS接收器在香港密集的城市峡谷中收集的两个数据集逐步验证了所提出的方法的有效性。提出了常规扩展的Kalman过滤器，几种现有方法和基于FGO的集成之间的比较。结果表明，现有的基于FGO的GNSS独立定位与PDR的相对运动估计高度互补。借助提出的方法获得了提高的定位精度和轨迹平滑度。
Accurate and smooth global navigation satellite system (GNSS) positioning for pedestrians in urban canyons is still a challenge due to the multipath effects and the non-light-of-sight (NLOS) receptions caused by the reflections from surrounding buildings. The recently developed factor graph optimization (FGO) based GNSS positioning method opened a new window for improving urban GNSS positioning by effectively exploiting the measurement redundancy from the historical information to resist the outlier measurements. Unfortunately, the FGO-based GNSS standalone positioning is still challenged in highly urbanized areas. As an extension of the previous FGO-based GNSS positioning method, this paper exploits the potential of the pedestrian dead reckoning (PDR) model in FGO to improve the GNSS standalone positioning performance in urban canyons. Specifically, the relative motion of the pedestrian is estimated based on the raw acceleration measurements from the onboard smartphone inertial measurement unit (IMU) via the PDR algorithm. Then the raw GNSS pseudorange, Doppler measurements, and relative motion from PDR are integrated using the FGO. Given the context of pedestrian navigation with a small acceleration most of the time, a novel soft motion model is proposed to smooth the states involved in the factor graph model. The effectiveness of the proposed method is verified step-by-step through two datasets collected in dense urban canyons of Hong Kong using smartphone-level GNSS receivers. The comparison between the conventional extended Kalman filter, several existing methods, and FGO-based integration is presented. The results reveal that the existing FGO-based GNSS standalone positioning is highly complementary to the PDR's relative motion estimation. Both improved positioning accuracy and trajectory smoothness are obtained with the help of the proposed method.
