  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.
