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：在未知环境下存在动态障碍物时的碰撞避免是无人系统面临的最关键挑战之一。本文提出了一种通过椭球体识别障碍物以估计其线速度和角速度的方法。我们的方法基于任何物体均可近似用椭球体表达的思想。为此，我们提出了一种融合高斯混合模型变分贝叶斯估计、Kyachiyan算法及精炼算法的方法。与现有基于优化的方法不同，本方法无需预知簇的数量且可实时运行。此外，我们定义了基于椭球体的特征向量，用于在时间相近的两帧点云间匹配障碍物。该方法可适用于包含静态与动态障碍物（包括旋转障碍物）的任何环境。通过与其他聚类方法的对比实验表明，当与轨迹规划器结合使用时，整体系统能够在存在动态障碍物的未知环境中高效穿行。