Accurate 6-DoF Motion Estimation for Irregular Moving Objects with Point Correlations

In the field of Simultaneous Localization and Map Building (SLAM), robots have become highly proficient in self-localization. However, the localization and tracking of irregular objects in the environment still pose significant challenges. Consequently, this work proposes a real-time approach to detect moving objects in the environment and estimate their six-degree-of-freedom (6-DoF) motion without making any assumptions about the type of the objects. The central idea is to analyze the correlation between map points and segmenting point clouds which are parts of dynamic objects belonging to different groups. Then, a region-based bundle adjustment method has been developed to obtain the optimized pose of the objects on the SE(3) manifold. Our method surpasses existing appearance-based approaches, which struggle to handle irregular objects. To validate the effectiveness of our algorithm, we tested our algorithm in real-world environments. The results demonstrate our method achieves superior accuracy in tracking dynamic objects, showcasing its potential for various applications.