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物的碰撞回避是无人系统面临的最重要的挑战之一。在本文中,我们提出一种以椭球形式识别障碍物,以估计线性和角障碍物速度的方法。我们的方法基于任何物体可以近似用椭球体现的想法。为此,我们提出了一种基于高斯混合模型的变分贝叶斯估计,Kyachiyan算法和一个优化算法的方法。与现有的基于优化的方法不同,我们的方法不需要知道聚类的数量并且可以实时操作。此外,我们定义了一个基于椭球的特征向量来匹配两个时间上关闭点帧的障碍物。我们的方法可以应用于任何具有静态和动态障碍物的环境,包括具有旋转障碍物的环境。我们将我们的算法与其他聚类方法进行比较,并表明当与轨迹规划器相结合时,整个系统可以在具有动态障碍物的未知环境中有效地遍历。