发表机构
University of California San Diego; Georgia Institute of Technology（加州大学圣迭戈分校; 佐治亚理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出将矩匹配概率数据关联与基于优化的SLAM结合，通过概率性多测量关联和虚拟测量模型，在假阴性和假阳性场景下提升iSAM2的定位性能。
AI 中文摘要
基于优化的同时定位与建图（SLAM）通过返回已知区域（回环闭合）来减少传感平台的累积导航误差。本文提出了一种将概率数据关联（PDA）与基于优化的SLAM相结合的方法。我们不再将单个测量与每个地标关联，而是遵循多目标跟踪领域的PDA范式。具体而言，在基于优化的SLAM的非线性最小二乘求解器之外增加的一个处理阶段中，我们的方法（i）将多个测量概率性地分配给地标，（ii）通过考虑多个测量与地标关联，利用矩匹配计算地标分布的均值和协方差，以及（iii）建立虚拟地标测量和相应的线性高斯测量模型，该模型产生与（ii）中矩匹配PDA相同的均值和协方差矩阵。通过将PDA更新步骤转换为等效的线性高斯测量更新步骤，PDA可以在任何基于优化的SLAM方法中有效执行。我们在包含假阴性和假阳性的场景中的初步数值评估表明，增量平滑与建图2（iSAM2）与所提出的PDA方法结合，相比传统iSAM2可以提高智能体定位性能。
英文摘要
Optimization-based simultaneous localization and mapping (SLAM) makes it possible to reduce accumulated navigation errors of sensing platforms by returning to known areas (loop closure). In this paper, we present an approach to combine probabilistic data association (PDA) with optimization-based SLAM. Instead of associating a single measurement with each landmark, we follow the PDA paradigm from the multiobject tracking community. In particular, in a processing stage performed in addition to the nonlinear least-squares solver of optimization-based SLAM, our method (i) assigns multiple measurements to landmarks probabilistically, (ii) computes the mean and covariance of landmark distributions via moment matching by taking multiple measurement-to-landmark associations into account, and (iii) establishes a virtual landmark measurement and a corresponding linear-Gaussian measurement model that leads to the mean and covariance matrix as moment-matching PDA in (ii). By converting the PDA update step into an equivalent linear-Gaussian measurement update step, PDA can be performed effectively within any optimization-based SLAM method. Our preliminary numerical evaluation in a scenario with false negatives and false positives indicates that incremental smoothing and mapping 2 (iSAM2), combined with the proposed PDA approach, can improve agent localization performance compared to conventional iSAM2.
Comments7 pages, 3 figures, ISIF FUSION 2026 Conference
