The online fusion and tracking of static objects from heterogeneous sensor detections is a fundamental problem in robotics, autonomous systems, and environmental mapping. Although classical data association approaches such as JPDA are well suited for dynamic targets, they are less effective for static objects observed intermittently and with heterogeneous uncertainties, where motion models provide minimal discriminative power with respect to clutter. In this paper, we propose a novel method for static object data association by clustering multi-modal sensor detections online (SODA-CitrON), while simultaneously estimating positions and maintaining persistent tracks for an unknown number of objects. The proposed unsupervised machine learning approach operates in a fully online manner and handles temporally uncorrelated and multi-sensor measurements. Additionally, it has a worst-case loglinear complexity in the number of sensor detections while providing full output explainability. We evaluate the proposed approach in different Monte Carlo simulation scenarios and compare it against state-of-the-art methods, including POM-based filtering, DBSTREAM clustering, and JPDA. The results demonstrate that SODA-CitrON consistently outperforms the compared methods in terms of F1 score, position RMSE, MOTP, and MOTA in the static object mapping scenarios studied.

翻译：异构传感器检测中静态目标的在线融合与跟踪是机器人、自主系统及环境建图领域的基本问题。尽管JPDA等经典数据关联方法适用于动态目标，但对于间歇性观测、存在异质不确定性且运动模型相对杂波区分能力极弱的静态目标，其有效性显著降低。本文提出一种基于在线聚类多模态传感器检测的静态目标数据关联新方法（SODA-CitrON），该方法可同步完成位置估计与未知数目目标的持续轨迹维护。所提出的无监督机器学习方法以完全在线方式运行，能处理时间非相关及多传感器测量，其最坏情况下的计算复杂度与传感器检测数量呈对数线性关系，同时保持完全输出可解释性。我们在不同蒙特卡洛仿真场景中评估该方法，并与基于POM滤波、DBSTREAM聚类及JPDA等现有先进方法进行对比。结果表明，在所研究的静态目标建图场景中，SODA-CitrON在F1分数、位置RMSE、MOTP及MOTA等指标上均持续优于对比方法。