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Extended Rigid Multi-Target Tracking in Dense Point Clouds with Probabilistic Occlusion Reasoning

机译：具有概率遮挡推理的密集点云中的扩展刚性多目标跟踪

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Tracking of multiple extended three-dimensional targets in the presence of partial occlusions from point cloud measurement sets remains a challenge but has obvious applications in autonomous vehicles. We demonstrate that existing range-image based approaches to point cloud segmentation and motion detection can be fused with fast point cloud registration techniques to aid track association. Consequently, multiple generic extended targets in point clouds can be tracked. We also show that robust occlusion handling can be achieved using temporally accumulated geometric information from co-registered point clouds in a static local reference frame. The occlusion logic is based on a novel probabilistic occlusion reasoning approach combined with a simulated geometric handling of self-occlusions. The proposed tracking pipeline was tested on synthetic target geometries superimposed on real-world backgrounds, as well as against measured data from benchmark autonomous driving datasets. The targets tracked encountered both artificially introduced and naturally occurring foreground occlusions as well as self-occlusions. Results show notable robustness to foreground occlusions. Incorrect track associations are possible when multiple targets in proximity display similar dynamics and possess nearly identical geometries. Our sequence of tracking algorithms relies on multiple tunable hyper-parameters. These parameters require further automated runtime-optimization before robust, real-time field application can be achieved.
机译：在存在来自点云测量集的部分遮挡的情况下跟踪多个扩展的三维目标仍然是一个挑战，但在自动驾驶汽车中有明显的应用。我们证明了现有的基于距离图像的点云分割和运动检测方法可以与快速点云配准技术融合，以辅助轨迹关联。因此，可以跟踪点云中的多个通用扩展目标。我们还显示，可以使用静态局部参考系中来自共同注册的点云的时间累积的几何信息来实现鲁棒的遮挡处理。遮挡逻辑基于一种新颖的概率遮挡推理方法，并结合了自遮挡的模拟几何处理。拟议的跟踪管道已在叠加在实际背景上的合成目标几何体上进行了测试，并与基准自动驾驶数据集中的测量数据进行了对比。跟踪的目标同时遇到了人工引入的和自然发生的前景遮挡以及自我遮挡。结果显示了对前景遮挡的显着鲁棒性。当接近中的多个目标显示相似的动力学并且具有几乎相同的几何形状时，可能会产生错误的轨迹关联。我们的跟踪算法序列依赖于多个可调超参数。这些参数需要进一步的自动化运行时优化，然后才能实现强大的实时现场应用。

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