A graph-based particle filter bathymetric simultaneous localization and mapping (BSLAM) method is proposed to solve the oscillation problem of the trajectories estimated by particles when using a low precise vehicle motion model and obtain accurate navigation results for autonomous underwater vehicles (AUVs). A graph-based trajectory update method is proposed to update the trajectories stored in particles before particle weighting to weaken the influence of the low precise odometer model on the particle trajectories. A particle weighting method based on submap matching is proposed to improve the robustness of the particle filter. Besides, a graph-based map generation method is proposed to solve the map selection problem of the particle filtering theory. The performance of the proposed method is demonstrated using a simulated dataset and a field dataset collected from a sea trial. The results show that the proposed method is more accurate and effective compared with a state-of-art particle filter BSLAM method.

中文翻译：

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基于图的轨迹更新方法的测深粒子滤波器 SLAM


提出一种基于图的粒子滤波测深同步定位与建图（BSLAM）方法，解决低精度航行器运动模型时粒子估计轨迹的振荡问题，获得自主水下航行器（AUV）的精确导航结果。提出一种基于图的轨迹更新方法，在粒子加权之前更新粒子中存储的轨迹，以减弱低精度里程计模型对粒子轨迹的影响。为了提高粒子滤波器的鲁棒性，提出了一种基于子图匹配的粒子加权方法。此外，提出了一种基于图的地图生成方法来解决粒子滤波理论的地图选择问题。使用模拟数据集和从海上试验收集的现场数据集证明了所提出方法的性能。结果表明，与最先进的粒子滤波器 BSLAM 方法相比，该方法更加准确和有效。