arXiv 2409.101432026-01-03cs.ROcs.CVeess.IV

P2U-SLAM：基于点不确定性与位姿不确定性的单目广视场SLAM系统

P2U-SLAM: A Monocular Wide-FoV SLAM System Based on Point Uncertainty and Pose Uncertainty

State Key Laboratory of Extreme Photonics and Instrumentation, Zhejiang University（极端光束与仪器国家重点实验室，浙江大学）；National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University（机器人视觉感知与控制技术国家工程研究中心，湖南大学）；State Key Laboratory of Autonomous Intelligent Unmanned Systems（自主智能无人系统国家重点实验室）

Yufan Zhang, Kailun Yang, Ze Wang, Kaiwei Wang

备注：: Accepted to IEEE Transactions on Intelligent Transportation Systems (T-ITS). The source code will be made publicly available at https://github.com/BambValley/P2U-SLAM

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AI总结：

P2U-SLAM通过引入点不确定性和位姿不确定性，提升广视场单目SLAM系统的鲁棒性和定位精度。

摘要：

本文提出了P2U-SLAM，一种基于广视场相机的视觉同时定位与建图（SLAM）系统，利用位姿不确定性和点不确定性。虽然广视场能够为历史地图点提供大量重复观测以匹配跨视图特征，但在优化过程中历史地图点的数据属性和关键帧的位姿已发生变化。忽视这些数据属性的变化会导致优化过程中信息矩阵缺失，增加长期定位性能退化的风险。本研究旨在缓解广视场视觉输入对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，将这些影响具体化为点不确定性和位姿不确定性，并给出了其具体的数学形式。P2U-SLAM将点不确定性嵌入跟踪模块，将位姿不确定性嵌入局部建图模块，并在每次优化操作（包括局部建图、地图融合和回环关闭）后更新这些不确定性。我们对两个流行公开数据集中的27个序列进行了全面评估，P2U-SLAM在与其他最新方法相比表现出优异的性能。源代码将在https://github.com/BambValley/P2U-SLAM上公开。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。