Collision avoidance in the presence of dynamic obstacles in unknown environments is one of the most critical challenges for unmanned systems. In this paper, we present a method that identifies obstacles in terms of ellipsoids to estimate linear and angular obstacle velocities. Our proposed method is based on the idea of any object can be approximately expressed by ellipsoids. To achieve this, we propose a method based on variational Bayesian estimation of Gaussian mixture model, the Kyachiyan algorithm, and a refinement algorithm. Our proposed method does not require knowledge of the number of clusters and can operate in real-time, unlike existing optimization-based methods. In addition, we define an ellipsoid-based feature vector to match obstacles given two timely close point frames. Our method can be applied to any environment with static and dynamic obstacles, including the ones with rotating obstacles. We compare our algorithm with other clustering methods and show that when coupled with a trajectory planner, the overall system can efficiently traverse unknown environments in the presence of dynamic obstacles.

翻译：在未知环境中存在动态障碍时避免碰撞是无人系统面临的最关键的挑战之一。在本文中,我们提出了一个方法,用以确定在光线和角障碍速度估计方面的障碍。我们提议的方法基于任何物体的概念,可以以光线和角障碍速度大致表达。为此,我们提出一种方法,其依据是对高萨混合物模型、Kyachiyan算法和精细算法进行不同波变的巴伊西亚估计。我们提议的方法并不要求了解集群数量,而且可以实时运行,与现有的优化方法不同。此外,我们定义了基于光线线和角障碍的矢量,以匹配两个及时的近点框架所设置的障碍。我们的方法可以适用于任何有静态和动态障碍的环境,包括有旋转障碍的环境。我们将我们的算法与其他集群方法进行比较,并表明,如果加上轨迹规划器,整个系统可以在存在动态障碍的情况下有效地穿越未知的环境。