论文标题
基于因子图的3D多对象跟踪在点云中
Factor Graph based 3D Multi-Object Tracking in Point Clouds
论文作者
论文摘要
3D空间中多个移动对象的准确跟踪是城市场景理解的重要组成部分。这是一个具有挑战性的任务，因为它需要在当前框架中分配检测到上一个对象的预测对象。如果此初始分配不正确，那么现有的基于过滤器的方法往往会挣扎，这很容易发生。我们提出了一种基于优化的新方法，该方法不依赖于明确和固定的作业。取而代之的是，我们代表了现成的3D对象检测器作为高斯混合模型的结果，该模型被纳入了因子图框架中。这使我们可以灵活地将所有检测分配给所有对象。结果，使用非线性最小二乘优化的3D空间多对象状态估计，分配问题被隐式和共同解决。尽管简单起见，但所提出的算法仍取得了可靠和可靠的跟踪结果，并且可以用于离线和在线跟踪。我们展示了它在现实世界中的Kitti跟踪数据集上的性能，并比许多最先进的算法取得了更好的结果。尤其是估计轨道的一致性是卓越的离线和在线。
Accurate and reliable tracking of multiple moving objects in 3D space is an essential component of urban scene understanding. This is a challenging task because it requires the assignment of detections in the current frame to the predicted objects from the previous one. Existing filter-based approaches tend to struggle if this initial assignment is not correct, which can happen easily. We propose a novel optimization-based approach that does not rely on explicit and fixed assignments. Instead, we represent the result of an off-the-shelf 3D object detector as Gaussian mixture model, which is incorporated in a factor graph framework. This gives us the flexibility to assign all detections to all objects simultaneously. As a result, the assignment problem is solved implicitly and jointly with the 3D spatial multi-object state estimation using non-linear least squares optimization. Despite its simplicity, the proposed algorithm achieves robust and reliable tracking results and can be applied for offline as well as online tracking. We demonstrate its performance on the real world KITTI tracking dataset and achieve better results than many state-of-the-art algorithms. Especially the consistency of the estimated tracks is superior offline as well as online.
