Detection and Tracking of General Movable Objects in Large Three-Dimensional Maps
Detection and Tracking of General Movable Objects in Large Three-Dimensional Maps
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DOI：
10.1109/tro.2018.2876111
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发表时间：
2019-02
影响因子：
7.8
通讯作者：
Nils Bore;Johan Ekekrantz;P. Jensfelt;J. Folkesson
中科院分区：
文献类型：
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作者：
Nils Bore;Johan Ekekrantz;P. Jensfelt;J. Folkesson
研究了大环境中运动的移动的机器人对具有半静态动态的一般目标的检测与跟踪问题。一个关键的问题是，由于环境的规模，机器人只能观察在任何给定的时间的对象的子集。由于在不同地方观察物体之间会经过一段时间，因此当机器人不在那里时，物体可能会移动。我们提出了一个模型，在这种运动中，对象通常只在局部移动，但有一些小的概率，他们通过我们所谓的全局运动跳跃更长的距离。对于过滤，我们将局部和全局运动的后验分解为两个链接的过程。对全局运动和测量关联的后验进行采样，同时使用卡尔曼滤波器分析跟踪局部运动。这种新的过滤器上的点云数据收集自主的移动的机器人在一段较长的时间内进行评估。我们表明，跟踪跳跃的对象是可行的，所提出的概率处理优于以前的方法时，适用于真实的世界数据。在这种情况下，有效的概率跟踪的关键是对象后验的集中采样。
This paper studies the problem of detection and tracking of general objects with semistatic dynamics observed by a mobile robot moving in a large environment. A key problem is that due to the environment scale, the robot can only observe a subset of the objects at any given time. Since some time passes between observations of objects in different places, the objects might be moved when the robot is not there. We propose a model for this movement in which the objects typically only move locally, but with some small probability they jump longer distances through what we call global motion. For filtering, we decompose the posterior over local and global movements into two linked processes. The posterior over the global movements and measurement associations is sampled, while we track the local movement analytically using Kalman filters. This novel filter is evaluated on point cloud data gathered autonomously by a mobile robot over an extended period of time. We show that tracking jumping objects is feasible, and that the proposed probabilistic treatment outperforms previous methods when applied to real world data. The key to efficient probabilistic tracking in this scenario is focused sampling of the object posteriors.