We propose an online spatiotemporal articulation model estimation framework that estimates both articulated structure as well as a temporal prediction model solely using passive observations. The resulting model can predict future mo- tions of an articulated object with high confidence because of the spatial and temporal structure. We demonstrate the effectiveness of the predictive model by incorporating it within a standard simultaneous localization and mapping (SLAM) pipeline for mapping and robot localization in previously unexplored dynamic environments. Our method is able to localize the robot and map a dynamic scene by explaining the observed motion in the world. We demonstrate the effectiveness of the proposed framework for both simulated and real-world dynamic environments.
@article{arxiv.1604.03526,
title = {Spatiotemporal Articulated Models for Dynamic SLAM},
author = {Suren Kumar and Vikas Dhiman and Madan Ravi Ganesh and Jason J. Corso},
journal= {arXiv preprint arXiv:1604.03526},
year = {2016}
}