Detecting and Tracking 6-DoF Motion of Unknown Dynamic Objects in Industrial Environments Using Stereo Visual Sensing

Despite recent advancements in robotic exploration, estimating the three-dimensional (3-D) motion of unknown dynamic objects, which are prevalent in chaotic construction sites, remains an unresolved challenge. In this work, we studied the problem of detecting and tracking the six-degree-of-freedom (6-DoF) motion of unknown moving objects using only a stereo camera. The fundamental idea of our approach is to estimate the positions of map points at each timestamp and model their uncertainty in position, such that the correlation between map points can be found by segmenting point clouds that are parts of moving objects into different groups. By analyzing the correlation between map points, we can detect the dynamic objects in the scene without making any assumptions about the type of objects. Thus, this approach enables tracking of both known and unknown moving objects, such as a robot carrier with stacked luggage. It surpasses the performance of existing appearance-based methods, which often face difficulties when dealing with unknown objects. Through extensive real-world experiments, we demonstrate the effectiveness of our approach in accurately tracking moving objects, highlighting its potential for various applications. In addition, we successfully deploy our object motion estimation algorithm in an unmanned ground vehicle (UGV) for the purpose of avoiding unknown moving objects in real-world scenarios. This practical implementation underscores the applicability of our approach in real-world settings.