Tracking extended objects based on measurements provided by light detection and ranging (LIDAR) and millimeter wave radio detection and ranging (RADAR) sensors is a key task to obtain situational awareness in important applications including autonomous driving and indoor robotics. In this paper, we propose probabilistic data association methods for localizing and tracking of extended objects that originate an unknown number of measurements. Our approach is based on factor graphs and the sum-product algorithm (SPA). In particular, we reduce computational complexity in a principled manner by means of “stretching” factors in the graph. After stretching, new variable and factor nodes have lower dimensions than the original nodes. This leads to a reduced computational complexity of the resulting SPA. One of the introduced methods is based on an overcomplete description of data association uncertainty and has a computational complexity that only scales quadratically in the number of objects and linearly in the number of measurements. Without relying on suboptimal preprocessing steps such as a clustering of measurements, it can localize and track multiple objects that potentially generate a large number of measurements. Simulation results confirm that despite their lower computational complexity, the proposed methods can outperform reference methods based on clustering.

中文翻译：

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用于扩展对象跟踪的可扩展数据关联


基于光探测和测距（LIDAR）和毫米波无线电探测和测距（RADAR）传感器提供的测量来跟踪扩展对象是在自动驾驶和室内机器人等重要应​​用中获得态势感知的关键任务。在本文中，我们提出了概率数据关联方法，用于定位和跟踪源自未知数量测量的扩展对象。我们的方法基于因子图和和积算法 (SPA)。特别是，我们通过图中的“拉伸”因子以原则性的方式降低计算复杂性。拉伸后，新的变量和因子节点的尺寸低于原始节点。这会降低生成的 SPA 的计算复杂性。所引入的方法之一基于对数据关联不确定性的过度完整描述，并且计算复杂性仅随对象数量呈二次方变化且随测量数量呈线性变化。在不依赖次优预处理步骤（例如测量聚类）的情况下，它可以定位和跟踪可能生成大量测量的多个对象。仿真结果证实，尽管计算复杂度较低，但所提出的方法可以优于基于聚类的参考方法。