Obstacle Identification and Ellipsoidal Decomposition for Fast Motion Planning in Unknown Dynamic Environments

TL;DR

This paper introduces a real-time obstacle identification method using ellipsoids and Bayesian estimation, enabling fast motion planning in unknown dynamic environments with static and rotating obstacles.

Contribution

It presents a novel ellipsoid-based obstacle representation and a Bayesian clustering approach that operates in real-time without prior knowledge of obstacle count.

Findings

Effective obstacle velocity estimation in dynamic environments

Real-time operation without prior cluster number knowledge

Successful integration with trajectory planning for navigation

Abstract

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.