基于LMB滤波器的目标参考点关联多假设跟踪
LMB Filter Based Tracking Allowing for Multiple Hypotheses in Object Reference Point Association*
摘要：
自动驾驶汽车需要精确了解周围的动态物体。特别是在具有许多物体和可能的遮挡的城市区域中，基于多传感器设置的基础设施系统可以为车辆提供所需的环境模型。之前，我们已经发布了对象参考点（例如，对象的角落）的概念，其允许通用传感器“即插即用”接口和相对便宜的传感器。本文介绍了一种新的方法，另外将多个假设融合的对象参考点的测量使用扩展到以前提出的标记多伯努利（LMB）滤波器。与以前的工作相比，这种方法提高了跟踪质量的情况下，正确的关联的测量和对象的参考点是未知的。此外，本文确定了基于物理模型的选项，以在早期阶段整理出不一致和不可行的关联，以保持该方法在计算上易于处理的实时应用。该方法进行了评估模拟以及真实的场景。与同类方法相比，所提出的方法显示出相当大的性能提高，特别是非连续轨道的数量显着减少。
英文摘要：
Autonomous vehicles need precise knowledge on dynamic objects in their surroundings. Especially in urban areas with many objects and possible occlusions, an infrastructure system based on a multi-sensor setup can provide the required environment model for the vehicles. Previously, we have published a concept of object reference points (e.g. the corners of an object), which allows for generic sensor "plug and play" interfaces and relatively cheap sensors. This paper describes a novel method to additionally incorporate multiple hypotheses for fusing the measurements of the object reference points using an extension to the previously presented Labeled Multi-Bernoulli (LMB) filter. In contrast to the previous work, this approach improves the tracking quality in the cases where the correct association of the measurement and the object reference point is unknown. Furthermore, this paper identifies options based on physical models to sort out inconsistent and unfeasible associations at an early stage in order to keep the method computationally tractable for real-time applications. The method is evaluated on simulations as well as on real scenarios. In comparison to comparable methods, the proposed approach shows a considerable performance increase, especially the number of non-continuous tracks is decreased significantly.
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