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、自主系统及环境测绘领域的基础问题。尽管JPDA等经典数据关联方法适用于动态目标，但对于间歇性观测且异质不确定性较大的静态目标——其运动模型对杂波的区分能力有限——这些方法效果欠佳。本文提出一种基于多模态传感器检测在线聚类的静态目标数据关联新方法（SODA-CitrON），该方法可在同步估计未知数量目标位置的同时维持持续跟踪轨迹。该无监督机器学习方法采用全在线运行模式，能处理时间不相关及多传感器测量数据。此外，其最坏情况下的计算复杂度对传感器检测数量呈对数线性，同时保持输出结果完全可解释性。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等前沿方法进行对比。结果表明，在研究的静态目标建图场景中，SODA-CitrON在F1分数、位置均方根误差、MOTP及MOTA指标上持续优于对比方法。