This paper presents P2U-SLAM, a visual Simultaneous Localization And Mapping (SLAM) system with a wide Field of View (FoV) camera, which utilizes pose uncertainty and point uncertainty. While the wide FoV enables considerable repetitive observations of historical map points for matching cross-view features, the data properties of the historical map points and the poses of historical keyframes have changed during the optimization process. The neglect of data property changes results in the lack of partial information matrices in optimization, increasing the risk of long-term positioning performance degradation. The purpose of our research is to mitigate the risks posed by wide-FoV visual input to the SLAM system. Based on the conditional probability model, this work reveals the definite impacts of the above data properties changes on the optimization process, concretizes these impacts as point uncertainty and pose uncertainty, and gives their specific mathematical form. P2U-SLAM embeds point uncertainty into the tracking module and pose uncertainty into the local mapping module respectively, and updates these uncertainties after each optimization operation including local mapping, map merging, and loop closing. We present an exhaustive evaluation on 27 sequences from two popular public datasets with wide-FoV visual input. P2U-SLAM shows excellent performance compared with other state-of-the-art methods. The source code will be made publicly available at https://github.com/BambValley/P2U-SLAM.

翻译：本文提出P2U-SLAM，一种基于位姿不确定性与点不确定性的宽视场视觉同时定位与建图系统。宽视场虽能实现对历史地图点的大量重复观测以匹配跨视角特征，但历史地图点的数据属性及历史关键帧位姿在优化过程中已发生改变。忽视数据属性变化会导致优化中部分信息矩阵缺失，增加长期定位性能下降的风险。本研究旨在缓解宽视场视觉输入对SLAM系统带来的风险。基于条件概率模型，本文揭示了上述数据属性变化对优化过程的确切影响，将其具体化为点不确定性与位姿不确定性，并给出了具体的数学表达形式。P2U-SLAM分别将点不确定性嵌入跟踪模块、位姿不确定性嵌入局部建图模块，并在每次包含局部建图、地图融合与闭环检测的优化操作后更新这些不确定性。我们在两个主流宽视场公开数据集的27个序列上进行了全面评估。相较于其他先进方法，P2U-SLAM展现出卓越性能。源代码将在https://github.com/BambValley/P2U-SLAM公开。